{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "baae3316",
   "metadata": {
    "ExecuteTime": {
     "start_time": "2022-04-27T08:36:58.638Z"
    }
   },
   "outputs": [],
   "source": [
    "# 原始链接： https://www.kaggle.com/code/abhayparashar31/titanic-complete-analysis-prediction/notebook#Model-Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "14cf5417",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T06:10:36.858229Z",
     "start_time": "2022-04-27T06:10:26.277515Z"
    }
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'pandas'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[3], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mseaborn\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01msns\u001b[39;00m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'pandas'"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08a69c36-b7f1-4a62-93f7-21a631ebb8c7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8aa043a0",
   "metadata": {},
   "source": [
    "## Loading Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be8fa395-7994-4a5d-9e92-6d951effb958",
   "metadata": {},
   "source": [
    "在机器学习中，数据集是指用于训练、验证和测试机器学习模型的数据的集合。数据集通常包含两个部分：特征（输入数据）和标签（输出数据）。下面我会详细介绍机器学习中常见的几种数据集：  \n",
    "\n",
    "**训练数据集（Training Dataset）：**  \n",
    "训练数据集是用于训练机器学习模型的数据集。  \n",
    "包含了特征和对应的标签。  \n",
    "模型通过学习训练数据集中的模式、关系和规律来进行训练。  \n",
    "**验证数据集（Validation Dataset）：**  \n",
    "验证数据集用于评估模型的性能和调优超参数。  \n",
    "通常从训练数据集中分出一部分作为验证数据集。  \n",
    "模型在验证数据集上进行评估，以便及时发现模型是否出现过拟合或欠拟合，并选择最佳的超参数设置。  \n",
    "**测试数据集（Test Dataset）：**  \n",
    "测试数据集用于最终评估模型的性能和泛化能力。  \n",
    "保持未见过的数据，用于评估模型在真实世界数据上的表现。  \n",
    "在模型训练和超参数调优完成后，使用测试数据集进行最终性能评估。  \n",
    "**无标签数据集（Unlabeled Dataset）：**  \n",
    "无标签数据集只包含特征信息，缺少对应的标签信息。  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "f65d839d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T06:16:05.279978Z",
     "start_time": "2022-04-27T06:16:05.228241Z"
    }
   },
   "outputs": [],
   "source": [
    "df_labeled = pd.read_csv (r'DataSet\\Titanic\\LabeledData.csv')\n",
    "df_unLabeled = pd.read_csv(r'DataSet\\Titanic\\UnlabeledData.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "8c869ec9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T06:16:31.809787Z",
     "start_time": "2022-04-27T06:16:31.687167Z"
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
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     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_labeled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "e36ae512",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_unLabeled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "89b7855f-9e79-48bd-801e-8ec3f73a2984",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T06:17:00.691323Z",
     "start_time": "2022-04-27T06:17:00.657741Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df_labeled.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "c85aae93",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T06:18:24.433704Z",
     "start_time": "2022-04-27T06:18:24.427106Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows :891 \n",
      "Number of columns:12\n"
     ]
    }
   ],
   "source": [
    "## Checking for  the number of rows and columns  in the dataset\n",
    "# 检查数据集中的行数和列数\n",
    "print(f\"Number of rows :{df_labeled.shape[0]} \\nNumber of columns:{df_labeled.shape[1]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89583cb4",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee6bbe99-c708-4b6e-bdd5-927d8a2571a3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T06:20:26.625771Z",
     "start_time": "2022-04-27T06:20:26.618401Z"
    }
   },
   "source": [
    "什么是EDA？ \n",
    "在kaggle社区经常会遇到EDA这个词。EDA的全称是Exploratory Data Analysis，是一种探索式的数据分析，  \n",
    "\n",
    "探索性数据分析（EDA）是由数据科学家用来分析和调查数据集，并总结其主要特征，通常采用数据可视化方法。  \n",
    "它有助于确定如何最好地操作数据源以获得你所需要的答案，使数据科学家更容易发现模式，发现异常，测试一个假设，或检查假设。  \n",
    "\n",
    "EDA的主要目的是目的是为了理解你的数据、做出任何假设之前帮助观察数据。它可以帮助识别明显的错误，以及更好地理解数据中的模式，检测异常值或异常事件，找到变量间的有趣关系。  \n",
    "\n",
    "通过总结数据的主要特征、绘制图表从而更形象生动的理解。  \n",
    "常用的作图方法有：直方图（Histograms），箱形图（Box plot），散点图（Scatter plot）等等。  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e98fd08c-8933-4b29-9221-e2f9920c5f44",
   "metadata": {},
   "source": [
    "为什么要做探索性数据分析？\n",
    "\n",
    "对数据集更深的理解（分布、缺失等统计信息）  \n",
    "获得高质量的数据集（异常值、缺失值的基本处理）  \n",
    "机器学习模型、实证假设构建的思路（灵感）  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "2064c134",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:17:04.697219Z",
     "start_time": "2022-04-27T07:17:04.680273Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数据集的列\n",
    "df_labeled.columns\n",
    "# 查看各个元素和Survived的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "b15239e3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T06:21:17.942796Z",
     "start_time": "2022-04-27T06:21:17.586410Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Survived\n",
    "ax = sns.countplot(data=df_labeled, x = 'Survived');  # countplot是seaborn库中分类图的一种，作用是使用条形显示每个分箱器中的观察计数\n",
    "ax.bar_label(ax.containers[0]) # 在柱状图上显示y的数值，取出AxesSubplot对象的containers属性，得到container对象的迭代器，遍历其或单独取出container对象传给plt.bar_label\n",
    "\n",
    "plt.title(\"Number of people Survived vs  Deceased\")\n",
    "plt.xlabel(\"Survived vs  Deceased\")\n",
    "plt.ylabel(\"Number of people\")\n",
    "plt.xticks(ticks=[0,1],labels=['Deceased','Survived'])\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "0101e884",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T06:30:38.066510Z",
     "start_time": "2022-04-27T06:30:38.050148Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Age\n",
    "df_labeled.Age.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "dfdd055e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T06:30:44.058081Z",
     "start_time": "2022-04-27T06:30:44.021465Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      22\n",
       "1      38\n",
       "2      26\n",
       "3      35\n",
       "4      35\n",
       "       ..\n",
       "886    27\n",
       "887    19\n",
       "888    29\n",
       "889    26\n",
       "890    32\n",
       "Name: Age, Length: 891, dtype: int32"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将Age为NAN的单元，使用mean平均值填充\n",
    "# df_labeled['Age']\n",
    "df_labeled['Age'] = df_labeled['Age'].fillna(df_labeled['Age'].mean()) # 使用mean值填充Age的NAN值\n",
    "df_labeled['Age'] = df_labeled['Age'].astype(int)\n",
    "df_labeled['Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "bee7acd9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:01:24.847181Z",
     "start_time": "2022-04-27T07:01:24.839637Z"
    }
   },
   "outputs": [],
   "source": [
    "# 将df_labeled进行拷贝\n",
    "temp = df_labeled .copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "5fb20453",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:01:25.785305Z",
     "start_time": "2022-04-27T07:01:25.769803Z"
    }
   },
   "outputs": [],
   "source": [
    "# 根据不同年龄段，打上不同的标签\n",
    "temp['Age'] = pd.cut(temp['Age'], bins = [0,12,20,40,120],labels=['Children','Teenage','Adult','Elder'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "07d78e4c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:01:26.711304Z",
     "start_time": "2022-04-27T07:01:26.328988Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '62'), Text(0, 0, '111'), Text(0, 0, '563'), Text(0, 0, '148')]"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 根据年龄段进行数据可视化\n",
    "ax = sns.countplot(data = temp, x='Age')\n",
    "ax.bar_label(ax.containers[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "fa5956e3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:01:27.327315Z",
     "start_time": "2022-04-27T07:01:26.788355Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 统计每个年龄段surived和not surived 的人数\n",
    "ax=sns.countplot(data = temp, x = 'Age',hue= 'Survived')  \n",
    "# data：df或array或array列表，用于绘图的数据集，x或y缺失时，data参数为数据集，同时x或y不可缺少，必须要有其中一个\n",
    "# x： x轴上的条形图，以x标签划分统计个数\n",
    "# hue：在x或y标签划分的同时，再以hue标签划分统计个数\n",
    "ax.bar_label(ax.containers[0]);\n",
    "ax.bar_label(ax.containers[1]);\n",
    "plt.legend(title='Survived or Not',loc='upper right',labels=['No','Yes']);\n",
    "plt.title('Age VS Survived')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "52916764",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:01:27.477134Z",
     "start_time": "2022-04-27T07:01:27.459457Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.204207968574636"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 票价fare\n",
    "# 票价的平均值\n",
    "temp['Fare'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "b65a6bfd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:01:30.887222Z",
     "start_time": "2022-04-27T07:01:30.419200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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c9dq4A+X2229XZ2enzpw5ox/96EeqqalRa2trvNvEpaGhQfX19dHHAwMDKiwsVEVFhTweT0KfKxwOKxAI6Mkj6QpF0hK699G1lQndLxWNzHfJkiVyuVzJvpxxh/k6i/k6i/k6y4b5jnwF5GrEHSiZmZm69dZbJUklJSU6fPiwvv/97+v+++/X4OCg+vv7Y15F6e3tlc/nkyT5fD4dOnQoZr+Rd/mMrLkct9stt9s96rjL5XJsyKFImkLDiQ0UPuE+4uTvHZiv05ivs5ivs5I533ied8z/DkokElEoFFJJSYlcLpdaWlqi57q7u9XT0yO/3y9J8vv96urqUl9fX3RNIBCQx+NRcXHxWC8FAACME3G9gtLQ0KClS5eqqKhIZ8+e1fbt2/Xmm2/q9ddfV05OjpYvX676+nrl5eXJ4/HokUcekd/vV1lZmSSpoqJCxcXFevDBB7Vp0yYFg0GtXr1adXV1l32FBAAA3JjiCpS+vj59/etf16lTp5STk6N58+bp9ddf15IlSyRJzzzzjNLT01VdXa1QKKTKyko9//zz0Y+fMGGCdu/erdraWvn9fk2aNEk1NTVav359Yu8KAACktLgCZcuWLVc8n5WVpaamJjU1NX3impkzZ/JuFgAAcEX8LB4AAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHXiCpTGxkZ9/vOf15QpU5Sfn697771X3d3dMWsuXryouro6TZ06VZMnT1Z1dbV6e3tj1vT09KiqqkrZ2dnKz8/XqlWrNDQ0NPa7AQAA40JcgdLa2qq6ujq1t7crEAgoHA6roqJC58+fj6557LHH9Morr2jnzp1qbW3VyZMndd9990XPDw8Pq6qqSoODgzpw4IBefPFFbdu2TWvWrEncXQEAgJSWEc/ivXv3xjzetm2b8vPz1dHRod///d/XmTNntGXLFm3fvl2LFi2SJG3dulV33HGH2tvbVVZWpubmZh0/flxvvPGGvF6v5s+frw0bNujxxx/X2rVrlZmZmbi7AwAAKSmuQPm4M2fOSJLy8vIkSR0dHQqHwyovL4+umT17toqKitTW1qaysjK1tbVp7ty58nq90TWVlZWqra3VsWPHtGDBglHPEwqFFAqFoo8HBgYkSeFwWOFweCy3MMrIfu50k9B9L937RjYyA2bhDObrLObrLObrLBvmG89zX3OgRCIRrVy5Ul/4whc0Z84cSVIwGFRmZqZyc3Nj1nq9XgWDweiaS+Nk5PzIuctpbGzUunXrRh1vbm5Wdnb2td7CFW1YGEn4nnv27En4nqkqEAgk+xLGNebrLObrLObrrGTO98KFC1e99poDpa6uTkePHtVbb711rVtctYaGBtXX10cfDwwMqLCwUBUVFfJ4PAl9rnA4rEAgoCePpCsUSUvo3kfXViZ0v1Q0Mt8lS5bI5XIl+3LGHebrLObrLObrLBvmO/IVkKtxTYGyYsUK7d69W/v379eMGTOix30+nwYHB9Xf3x/zKkpvb698Pl90zaFDh2L2G3mXz8iaj3O73XK73aOOu1wux4YciqQpNJzYQOET7iNO/t6B+TqN+TqL+TormfON53njehePMUYrVqzQrl27tG/fPs2aNSvmfElJiVwul1paWqLHuru71dPTI7/fL0ny+/3q6upSX19fdE0gEJDH41FxcXE8lwMAAMapuF5Bqaur0/bt2/XjH/9YU6ZMiX7PSE5OjiZOnKicnBwtX75c9fX1ysvLk8fj0SOPPCK/36+ysjJJUkVFhYqLi/Xggw9q06ZNCgaDWr16terq6i77KgkAALjxxBUomzdvliR9+ctfjjm+detW/cmf/Ikk6ZlnnlF6erqqq6sVCoVUWVmp559/Prp2woQJ2r17t2pra+X3+zVp0iTV1NRo/fr1Y7sTAAAwbsQVKMZ8+ltvs7Ky1NTUpKampk9cM3PmTN7RAgAAPhE/iwcAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGCduH5YIMbmlidedWzv9zZWObY3AADXG6+gAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOnEHyv79+3X33XeroKBAaWlpevnll2POG2O0Zs0aTZ8+XRMnTlR5ebneeeedmDWnT5/WsmXL5PF4lJubq+XLl+vcuXNjuhEAADB+xB0o58+f15133qmmpqbLnt+0aZOee+45vfDCCzp48KAmTZqkyspKXbx4Mbpm2bJlOnbsmAKBgHbv3q39+/fr4Ycfvva7AAAA40pGvB+wdOlSLV269LLnjDF69tlntXr1at1zzz2SpH/4h3+Q1+vVyy+/rK997Wv6xS9+ob179+rw4cNauHChJOkHP/iBvvrVr+qv//qvVVBQMIbbAQAA40FCvwflxIkTCgaDKi8vjx7LyclRaWmp2traJEltbW3Kzc2NxokklZeXKz09XQcPHkzk5QAAgBQV9ysoVxIMBiVJXq835rjX642eCwaDys/Pj72IjAzl5eVF13xcKBRSKBSKPh4YGJAkhcNhhcPhhF3/yJ6S5E43Cd3XaYmeg1NGrjNVrjfVMF9nMV9nMV9n2TDfeJ47oYHilMbGRq1bt27U8ebmZmVnZzvynBsWRhzZ1yl79uxJ9iXEJRAIJPsSxjXm6yzm6yzm66xkzvfChQtXvTahgeLz+SRJvb29mj59evR4b2+v5s+fH13T19cX83FDQ0M6ffp09OM/rqGhQfX19dHHAwMDKiwsVEVFhTweTyJvQeFwWIFAQE8eSVcokpbQvZ10dG1lsi/hqozMd8mSJXK5XMm+nHGH+TqL+TqL+TrLhvmOfAXkaiQ0UGbNmiWfz6eWlpZokAwMDOjgwYOqra2VJPn9fvX396ujo0MlJSWSpH379ikSiai0tPSy+7rdbrnd7lHHXS6XY0MORdIUGk6dQEm1T2Ynf+/AfJ3GfJ3FfJ2VzPnG87xxB8q5c+f07rvvRh+fOHFCnZ2dysvLU1FRkVauXKnvfOc7uu222zRr1iw9+eSTKigo0L333itJuuOOO/SVr3xFDz30kF544QWFw2GtWLFCX/va13gHDwAAkHQNgXLkyBH94R/+YfTxyJdeampqtG3bNv35n/+5zp8/r4cfflj9/f364he/qL179yorKyv6Mf/0T/+kFStWaPHixUpPT1d1dbWee+65BNwOAAAYD+IOlC9/+csy5pPf4ZKWlqb169dr/fr1n7gmLy9P27dvj/epAQDADYKfxQMAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOtkJPsCkBi3PPGqI/u+t7HKkX0BALgSXkEBAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANbJSPYFwG63PPFqQvdzTzDadJc0Z+3r6v7u/0no3gCA8YNXUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHX4acYYdxL9E5gv9d7GKsf2BgB8hEBB0jgZEgCA1MaXeAAAgHUIFAAAYB0CBQAAWIfvQQHGOb5pGEAqSuorKE1NTbrllluUlZWl0tJSHTp0KJmXAwAALJG0V1D+5V/+RfX19XrhhRdUWlqqZ599VpWVleru7lZ+fn6yLguABXjVB0DSAuXpp5/WQw89pG984xuSpBdeeEGvvvqqfvjDH+qJJ55I1mUBAJAy4ol59wSjTXdJc9a+rtBw2qeuT3bMJyVQBgcH1dHRoYaGhuix9PR0lZeXq62tbdT6UCikUCgUfXzmzBlJ0unTpxUOhxN6beFwWBcuXFBGOF3DkU//DUR8MiJGFy5EUna+t377JUf2PdiwOCH7jPz5/eCDD+RyuSRJGUPnE7L35Tg1Dyf/Yvrggw+u+WMvN18kDvONXzyf3/H+/TuWz5VPcvbsWUmSMeZT1yYlUH7zm99oeHhYXq835rjX69Uvf/nLUesbGxu1bt26UcdnzZrl2DXCOf832RdgoWn/L9lXcONg1riRxfP3r5OfK2fPnlVOTs4V16TEu3gaGhpUX18ffRyJRHT69GlNnTpVaWmJ/X/hAwMDKiws1Pvvvy+Px5PQvcF8ncZ8ncV8ncV8nWXDfI0xOnv2rAoKCj51bVICZdq0aZowYYJ6e3tjjvf29srn841a73a75Xa7Y47l5uY6eYnyeDx8gjiI+TqL+TqL+TqL+Tor2fP9tFdORiTlbcaZmZkqKSlRS0tL9FgkElFLS4v8fn8yLgkAAFgkaV/iqa+vV01NjRYuXKi77rpLzz77rM6fPx99Vw8AALhxJS1Q7r//fv3617/WmjVrFAwGNX/+fO3du3fUN85eb263W0899dSoLykhMZivs5ivs5ivs5ivs1Jtvmnmat7rAwAAcB3xwwIBAIB1CBQAAGAdAgUAAFiHQAEAANYhUC7R1NSkW265RVlZWSotLdWhQ4eSfUkpYf/+/br77rtVUFCgtLQ0vfzyyzHnjTFas2aNpk+frokTJ6q8vFzvvPNOzJrTp09r2bJl8ng8ys3N1fLly3Xu3LnreBf2amxs1Oc//3lNmTJF+fn5uvfee9Xd3R2z5uLFi6qrq9PUqVM1efJkVVdXj/qHEHt6elRVVaXs7Gzl5+dr1apVGhoaup63YqXNmzdr3rx50X+8yu/367XXXoueZ7aJs3HjRqWlpWnlypXRY8x3bNauXau0tLSYX7Nnz46eT+n5GhhjjNmxY4fJzMw0P/zhD82xY8fMQw89ZHJzc01vb2+yL816e/bsMX/5l39p/vVf/9VIMrt27Yo5v3HjRpOTk2Nefvll8x//8R/mj/7oj8ysWbPMb3/72+iar3zlK+bOO+807e3t5t///d/Nrbfeah544IHrfCd2qqysNFu3bjVHjx41nZ2d5qtf/aopKioy586di6755je/aQoLC01LS4s5cuSIKSsrM7/3e78XPT80NGTmzJljysvLzdtvv2327Nljpk2bZhoaGpJxS1b5t3/7N/Pqq6+a//zP/zTd3d3mL/7iL4zL5TJHjx41xjDbRDl06JC55ZZbzLx588yjjz4aPc58x+app54yn/3sZ82pU6eiv379619Hz6fyfAmU/3XXXXeZurq66OPh4WFTUFBgGhsbk3hVqefjgRKJRIzP5zPf+973osf6+/uN2+02//zP/2yMMeb48eNGkjl8+HB0zWuvvWbS0tLM//zP/1y3a08VfX19RpJpbW01xnw4T5fLZXbu3Bld84tf/MJIMm1tbcaYDyMyPT3dBIPB6JrNmzcbj8djQqHQ9b2BFHDTTTeZv//7v2e2CXL27Flz2223mUAgYP7gD/4gGijMd+yeeuopc+edd172XKrPly/xSBocHFRHR4fKy8ujx9LT01VeXq62trYkXlnqO3HihILBYMxsc3JyVFpaGp1tW1ubcnNztXDhwuia8vJypaen6+DBg9f9mm135swZSVJeXp4kqaOjQ+FwOGbGs2fPVlFRUcyM586dG/MPIVZWVmpgYEDHjh27jldvt+HhYe3YsUPnz5+X3+9ntglSV1enqqqqmDlK/NlNlHfeeUcFBQX63d/9XS1btkw9PT2SUn++KfHTjJ32m9/8RsPDw6P+FVuv16tf/vKXSbqq8SEYDErSZWc7ci4YDCo/Pz/mfEZGhvLy8qJr8KFIJKKVK1fqC1/4gubMmSPpw/llZmaO+gGaH5/x5X4PRs7d6Lq6uuT3+3Xx4kVNnjxZu3btUnFxsTo7O5ntGO3YsUM/+9nPdPjw4VHn+LM7dqWlpdq2bZtuv/12nTp1SuvWrdOXvvQlHT16NOXnS6AAKaSurk5Hjx7VW2+9lexLGVduv/12dXZ26syZM/rRj36kmpoatba2JvuyUt7777+vRx99VIFAQFlZWcm+nHFp6dKl0f+eN2+eSktLNXPmTL300kuaOHFiEq9s7PgSj6Rp06ZpwoQJo76zube3Vz6fL0lXNT6MzO9Ks/X5fOrr64s5PzQ0pNOnTzP/S6xYsUK7d+/WT37yE82YMSN63OfzaXBwUP39/THrPz7jy/0ejJy70WVmZurWW29VSUmJGhsbdeedd+r73/8+sx2jjo4O9fX16XOf+5wyMjKUkZGh1tZWPffcc8rIyJDX62W+CZabm6vPfOYzevfdd1P+zy+Bog//ciopKVFLS0v0WCQSUUtLi/x+fxKvLPXNmjVLPp8vZrYDAwM6ePBgdLZ+v1/9/f3q6OiIrtm3b58ikYhKS0uv+zXbxhijFStWaNeuXdq3b59mzZoVc76kpEQulytmxt3d3erp6YmZcVdXV0wIBgIBeTweFRcXX58bSSGRSEShUIjZjtHixYvV1dWlzs7O6K+FCxdq2bJl0f9mvol17tw5/epXv9L06dNT/89vUr9F1yI7duwwbrfbbNu2zRw/ftw8/PDDJjc3N+Y7m3F5Z8+eNW+//bZ5++23jSTz9NNPm7ffftv813/9lzHmw7cZ5+bmmh//+Mfm5z//ubnnnnsu+zbjBQsWmIMHD5q33nrL3HbbbbzN+H/V1taanJwc8+abb8a8lfDChQvRNd/85jdNUVGR2bdvnzly5Ijx+/3G7/dHz4+8lbCiosJ0dnaavXv3mptvvtmKtxIm2xNPPGFaW1vNiRMnzM9//nPzxBNPmLS0NNPc3GyMYbaJdum7eIxhvmP1rW99y7z55pvmxIkT5qc//akpLy8306ZNM319fcaY1J4vgXKJH/zgB6aoqMhkZmaau+66y7S3tyf7klLCT37yEyNp1K+amhpjzIdvNX7yySeN1+s1brfbLF682HR3d8fs8cEHH5gHHnjATJ482Xg8HvONb3zDnD17Ngl3Y5/LzVaS2bp1a3TNb3/7W/Nnf/Zn5qabbjLZ2dnmj//4j82pU6di9nnvvffM0qVLzcSJE820adPMt771LRMOh6/z3djnT//0T83MmTNNZmamufnmm83ixYujcWIMs020jwcK8x2b+++/30yfPt1kZmaa3/md3zH333+/effdd6PnU3m+acYYk5zXbgAAAC6P70EBAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABY5/8D6FblI8cVY2oAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp['Fare'].hist(bins=20)  # Pandas的画图函数：df.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "bbbc38a0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:01:34.042756Z",
     "start_time": "2022-04-27T07:01:32.830800Z"
    },
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 根据票价，分为四个分段 \n",
    "temp['Fare'] = pd.cut(temp['Fare'], bins=[0,8,16,32,110], labels=['Low_fare','median_fare','Average_fare','high_fare'])\n",
    "ax = sns.countplot(data=temp, x='Fare')\n",
    "ax.bar_label(ax.containers[0]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "a7a14e4a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T06:52:33.768934Z",
     "start_time": "2022-04-27T06:52:33.254281Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 各个票价分段，存活和死亡的人数\n",
    "ax = sns.countplot(data=temp, x='Fare',hue='Survived')\n",
    "ax.bar_label(ax.containers[0]);\n",
    "ax.bar_label(ax.containers[1]);\n",
    "plt.legend(title='Survived or Not', loc='upper right', labels=['No', 'Yes']);\n",
    "plt.title('Fare Vs Survived')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "f2f9cce7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:01:35.549838Z",
     "start_time": "2022-04-27T07:01:35.539340Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "687"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cabin座位信息\n",
    "nullCabin = temp.Cabin.isna().sum()\n",
    "nullCabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "d8a254e1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:01:37.321794Z",
     "start_time": "2022-04-27T07:01:37.310060Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       NaN\n",
       "1       C85\n",
       "2       NaN\n",
       "3      C123\n",
       "4       NaN\n",
       "       ... \n",
       "886     NaN\n",
       "887     B42\n",
       "888     NaN\n",
       "889    C148\n",
       "890     NaN\n",
       "Name: Cabin, Length: 891, dtype: object"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp['Cabin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "381544ac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:01:37.810423Z",
     "start_time": "2022-04-27T07:01:37.801999Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'77.1% Null Values'"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 座位信息缺失严重\n",
    "str(round(nullCabin/(len(temp))*100,2))+\"% Null Values\"\n",
    "# 有这么多空值，所以最好放弃它。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "877fd54c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:01:38.330627Z",
     "start_time": "2022-04-27T07:01:38.320124Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 2], dtype=int64)"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 仓位等级 Pclass : Passenger Class\n",
    "temp.Pclass.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "5ec834bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:01:39.203432Z",
     "start_time": "2022-04-27T07:01:38.879217Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(data=temp,x='Pclass');\n",
    "ax.bar_label(ax.containers[0]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "692ac14c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:01:39.959469Z",
     "start_time": "2022-04-27T07:01:39.482502Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(data=temp,x='Pclass',hue='Survived');\n",
    "ax.bar_label(ax.containers[0]);\n",
    "ax.bar_label(ax.containers[1]);\n",
    "plt.legend(title='Survived or Not', loc='upper left', labels=['No', 'Yes']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "aa490727",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:02:57.064000Z",
     "start_time": "2022-04-27T07:02:57.050334Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['male', 'female'], dtype=object)"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sex\n",
    "# 查看Sex的值有哪些\n",
    "temp.Sex.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "b83a07ad",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:04:17.584207Z",
     "start_time": "2022-04-27T07:04:17.282648Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oUFJSkl5//XXv9uDgYK1bt05paWlyOp1q2rSpUlNTNWPGDD/OGgAABBKLx+Px+HsS/uZ2u2Wz2VRWVlZn9+90m7iiTo4LNHT5rwz39xQANFBX+/fb79+zAwAAUJeIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AQEBbsGCBOnfuLKvVKqvVKqfTqQ0bNni3v/nmm+rTp4+sVqssFotKS0uveKyKigp16dJFFotF+/btq/vJIyAQOwCAgNayZUu99NJLys/P1969e9W3b1/de++9OnDggCTpzJkzuvvuu/Xss8/+5LEmTZqkuLi4up4yAkyNYqdv376XLWe3262+ffte65wAAPAaPHiw7rnnHrVt21Y33nijXnzxRUVERCgvL0+SNG7cOD3zzDPq2bPnjx5nw4YN2rRpk/74xz/Wx7QRQEJqstO2bdtUWVl5yfpz587p448/vuZJAQBwORcuXNDq1at1+vRpOZ3Oq96vuLhYI0eO1Pvvv68mTZrU4QwRiH5W7Pzv//6v958PHjwol8vlfXzhwgVlZ2frV7/6Ve3NDgAASfv375fT6dS5c+cUERGhtWvXKjEx8ar29Xg8evTRR/Xkk0+qe/fu+uqrr+p2sgg4Pyt2Lt7UZbFYLvt2VXh4uObNm1drkwMAQJLatWunffv2qaysTGvWrFFqaqq2b99+VcEzb948lZeXKzMzsx5mikD0s2Ln2LFj8ng8atOmjXbv3q2YmBjvttDQUMXGxio4OLjWJwkA+GULDQ3VDTfcIEnq1q2b9uzZo9dee01vvPHGT+774YcfKjc3V2FhYT7ru3fvrpSUFC1fvrxO5ozA8bNiJyEhQZJUXV1dJ5MBAOBqVFdXq6Ki4qrGzp07Vy+88IL38fHjx5WUlKR33nlHPXr0qKspIoDU6AZlSTp8+LC2bt2qkpKSS+Jn6tSp1zwxAAAkKTMzUwMHDlSrVq1UXl6ulStXatu2bdq4caMkyeVyyeVy6ciRI5K+v78nMjJSrVq1UnR0tFq1auVzvIiICEnSr3/9a7Vs2bJ+TwZ+UaPYWbRokdLS0tS8eXM5HA5ZLBbvNovFQuwAAGpNSUmJhg8frqKiItlsNnXu3FkbN27UXXfdJUlauHChpk+f7h1/xx13SJKWLl2qRx991B9TRoCxeDwez8/dKSEhQaNHj9bkyZPrYk71zu12y2azqaysTFartU6eo9vEFXVyXKChy39luL+nAKCButq/3zX6UsHvvvtOQ4YMqfHkAAAA6kuN3sYaMmSINm3apCeffLK25wMADQ5XboHLC5QrtzWKnRtuuEFTpkxRXl6eOnXqpEaNGvlsf+qpp2plcgAAANeqRrHz5ptvKiIiQtu3b9f27dt9tlksFmIHAAAEjBrFzrFjx2p7HgAAAHWiRjcoAwAANBQ1urLz+OOP/+j2JUuWXNVxFixYoAULFnh/lO2mm27S1KlTNXDgQEnf/4r6008/rVWrVqmiokJJSUl6/fXXZbfbvccoKChQWlqatm7dqoiICKWmpiorK0shITX+vkQAAGCQGhXBd9995/O4qqpKX3zxhUpLSy/7A6FX0rJlS7300ktq27atPB6Pli9frnvvvVefffaZbrrpJo0fP17r16/X6tWrZbPZNGbMGD3wwAPasWOHpO9/aX3QoEFyOBzauXOnioqKNHz4cDVq1EizZs2qyakBAADD1Ch21q5de8m66upqpaWl6de//vVVH2fw4ME+j1988UUtWLBAeXl5atmypRYvXqyVK1d6A2rp0qXq0KGD8vLy1LNnT23atEkHDx7Uli1bZLfb1aVLF82cOVOTJ0/WtGnTFBoaWpPTAwAABqm1e3aCgoKUkZGhOXPm1Gj/CxcuaNWqVTp9+rScTqfy8/NVVVWl/v37e8e0b99erVq1Um5uriQpNzdXnTp18nlbKykpSW63WwcOHLjic1VUVMjtdvssAADATLV6g/LRo0d1/vz5n7XP/v37FRERobCwMD355JNau3atEhMT5XK5FBoaqqioKJ/xdrtdLpdL0vc//vbPoXNx+8VtV5KVlSWbzeZd4uPjf9acAQBAw1Gjt7EyMjJ8Hns8HhUVFWn9+vVKTU39Wcdq166d9u3bp7KyMq1Zs0apqamXfHdPbcvMzPQ5B7fbTfAAAGCoGsXOZ5995vM4KChIMTEx+tOf/vSTn9T6odDQUN1www2SpG7dumnPnj167bXX9NBDD6myslKlpaU+V3eKi4vlcDgkSQ6HQ7t37/Y5XnFxsXfblYSFhSksLOxnzRMAADRMNYqdrVu31vY8vKqrq1VRUaFu3bqpUaNGysnJUXJysiTp0KFDKigokNPplCQ5nU69+OKLKikpUWxsrCRp8+bNslqtSkxMrLM5AgCAhuOavozmxIkTOnTokKTv346KiYn5WftnZmZq4MCBatWqlcrLy7Vy5Upt27ZNGzdulM1m04gRI5SRkaHo6GhZrVaNHTtWTqdTPXv2lCQNGDBAiYmJGjZsmGbPni2Xy6XnnntO6enpXLkBAACSahg7p0+f1tixY7VixQpVV1dLkoKDgzV8+HDNmzdPTZo0uarjlJSUaPjw4SoqKpLNZlPnzp21ceNG3XXXXZKkOXPmKCgoSMnJyT5fKnhRcHCw1q1bp7S0NDmdTjVt2lSpqamaMWNGTU4LAAAYqMY3KG/fvl0ffPCBbr/9dknSJ598oqeeekpPP/20FixYcFXHWbx48Y9ub9y4sebPn6/58+dfcUxCQoL++te/Xv3kAQDAL0qNYue9997TmjVr1KdPH++6e+65R+Hh4XrwwQevOnYAAADqWo2+Z+fMmTOXfL+NJMXGxurMmTPXPCkAAIDaUqPYcTqdev7553Xu3DnvurNnz2r69OneT0oBAAAEghq9jfXqq6/q7rvvVsuWLXXzzTdLkj7//HOFhYVp06ZNtTpBAACAa1Gj2OnUqZMOHz6st99+W3/7298kSUOHDlVKSorCw8NrdYIAAADXokaxk5WVJbvdrpEjR/qsX7JkiU6cOKHJkyfXyuQAAACuVY3u2XnjjTfUvn37S9bfdNNNWrhw4TVPCgAAoLbUKHZcLpdatGhxyfqYmBgVFRVd86QAAABqS41iJz4+Xjt27Lhk/Y4dOxQXF3fNkwIAAKgtNbpnZ+TIkRo3bpyqqqrUt29fSVJOTo4mTZqkp59+ulYnCAAAcC1qFDsTJ07UyZMnNXr0aFVWVkr6/qcdJk+erMzMzFqdIAAAwLWoUexYLBa9/PLLmjJlir788kuFh4erbdu2/NI4AAAIODWKnYsiIiJ066231tZcAAAAal2NblAGAABoKIgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0fwaO1lZWbr11lsVGRmp2NhY3XfffTp06JDPmHPnzik9PV3NmjVTRESEkpOTVVxc7DOmoKBAgwYNUpMmTRQbG6uJEyfq/Pnz9XkqAAAgQPk1drZv36709HTl5eVp8+bNqqqq0oABA3T69GnvmPHjx+uDDz7Q6tWrtX37dh0/flwPPPCAd/uFCxc0aNAgVVZWaufOnVq+fLmWLVumqVOn+uOUAABAgAnx55NnZ2f7PF62bJliY2OVn5+vO+64Q2VlZVq8eLFWrlypvn37SpKWLl2qDh06KC8vTz179tSmTZt08OBBbdmyRXa7XV26dNHMmTM1efJkTZs2TaGhof44NQAAECAC6p6dsrIySVJ0dLQkKT8/X1VVVerfv793TPv27dWqVSvl5uZKknJzc9WpUyfZ7XbvmKSkJLndbh04cOCyz1NRUSG32+2zAAAAMwVM7FRXV2vcuHG6/fbb1bFjR0mSy+VSaGiooqKifMba7Xa5XC7vmH8OnYvbL267nKysLNlsNu8SHx9fy2cDAAACRcDETnp6ur744gutWrWqzp8rMzNTZWVl3qWwsLDOnxMAAPiHX+/ZuWjMmDFat26dPvroI7Vs2dK73uFwqLKyUqWlpT5Xd4qLi+VwOLxjdu/e7XO8i5/Wujjmh8LCwhQWFlbLZwEAAAKRX6/seDwejRkzRmvXrtWHH36o1q1b+2zv1q2bGjVqpJycHO+6Q4cOqaCgQE6nU5LkdDq1f/9+lZSUeMds3rxZVqtViYmJ9XMiAAAgYPn1yk56erpWrlyp//7v/1ZkZKT3Hhubzabw8HDZbDaNGDFCGRkZio6OltVq1dixY+V0OtWzZ09J0oABA5SYmKhhw4Zp9uzZcrlceu6555Sens7VGwAA4N/YWbBggSSpT58+PuuXLl2qRx99VJI0Z84cBQUFKTk5WRUVFUpKStLrr7/uHRscHKx169YpLS1NTqdTTZs2VWpqqmbMmFFfpwEAAAKYX2PH4/H85JjGjRtr/vz5mj9//hXHJCQk6K9//WttTg0AABgiYD6NBQAAUBeIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDS/xs5HH32kwYMHKy4uThaLRe+//77Pdo/Ho6lTp6pFixYKDw9X//79dfjwYZ8x3377rVJSUmS1WhUVFaURI0bo1KlT9XgWAAAgkPk1dk6fPq2bb75Z8+fPv+z22bNna+7cuVq4cKF27dqlpk2bKikpSefOnfOOSUlJ0YEDB7R582atW7dOH330kUaNGlVfpwAAAAJciD+ffODAgRo4cOBlt3k8Hr366qt67rnndO+990qSVqxYIbvdrvfff18PP/ywvvzyS2VnZ2vPnj3q3r27JGnevHm655579Mc//lFxcXH1di4AACAwBew9O8eOHZPL5VL//v2962w2m3r06KHc3FxJUm5urqKioryhI0n9+/dXUFCQdu3adcVjV1RUyO12+ywAAMBMARs7LpdLkmS3233W2+127zaXy6XY2Fif7SEhIYqOjvaOuZysrCzZbDbvEh8fX8uzBwAAgSJgY6cuZWZmqqyszLsUFhb6e0oAAKCOBGzsOBwOSVJxcbHP+uLiYu82h8OhkpISn+3nz5/Xt99+6x1zOWFhYbJarT4LAAAwU8DGTuvWreVwOJSTk+Nd53a7tWvXLjmdTkmS0+lUaWmp8vPzvWM+/PBDVVdXq0ePHvU+ZwAAEHj8+mmsU6dO6ciRI97Hx44d0759+xQdHa1WrVpp3LhxeuGFF9S2bVu1bt1aU6ZMUVxcnO677z5JUocOHXT33Xdr5MiRWrhwoaqqqjRmzBg9/PDDfBILAABI8nPs7N27V3feeaf3cUZGhiQpNTVVy5Yt06RJk3T69GmNGjVKpaWl6tWrl7Kzs9W4cWPvPm+//bbGjBmjfv36KSgoSMnJyZo7d269nwsAAAhMfo2dPn36yOPxXHG7xWLRjBkzNGPGjCuOiY6O1sqVK+tiegAAwAABe88OAABAbSB2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YyJnfnz5+v6669X48aN1aNHD+3evdvfUwIAAAHAiNh55513lJGRoeeff16ffvqpbr75ZiUlJamkpMTfUwMAAH5mROz8+c9/1siRI/XYY48pMTFRCxcuVJMmTbRkyRJ/Tw0AAPhZiL8ncK0qKyuVn5+vzMxM77qgoCD1799fubm5l92noqJCFRUV3sdlZWWSJLfbXWfzvFBxts6ODTRkdfm6qy+8voHLq+vX98XjezyeHx3X4GPnm2++0YULF2S3233W2+12/e1vf7vsPllZWZo+ffol6+Pj4+tkjgCuzDbvSX9PAUAdqa/Xd3l5uWw22xW3N/jYqYnMzExlZGR4H1dXV+vbb79Vs2bNZLFY/Dgz1Ae32634+HgVFhbKarX6ezoAahGv718Wj8ej8vJyxcXF/ei4Bh87zZs3V3BwsIqLi33WFxcXy+FwXHafsLAwhYWF+ayLioqqqykiQFmtVv5jCBiK1/cvx49d0bmowd+gHBoaqm7duiknJ8e7rrq6Wjk5OXI6nX6cGQAACAQN/sqOJGVkZCg1NVXdu3fXb37zG7366qs6ffq0HnvsMX9PDQAA+JkRsfPQQw/pxIkTmjp1qlwul7p06aLs7OxLbloGpO/fxnz++ecveSsTQMPH6xuXY/H81Oe1AAAAGrAGf88OAADAjyF2AACA0YgdAABgNGIH+H8effRR3Xffff6eBvCL4PF4NGrUKEVHR8tisWjfvn1+mcdXX33l1+dH/TDi01gAgIYlOztby5Yt07Zt29SmTRs1b97c31OCwYgdAEC9O3r0qFq0aKHbbrvN31PBLwBvY6FB6tOnj8aOHatx48bpuuuuk91u16JFi7xfJhkZGakbbrhBGzZskCRduHBBI0aMUOvWrRUeHq527drptdde+9HnqK6uVlZWlnefm2++WWvWrKmP0wOM9uijj2rs2LEqKCiQxWLR9ddf/5Ovt23btslisWjjxo265ZZbFB4err59+6qkpEQbNmxQhw4dZLVa9fvf/15nzpzx7pedna1evXopKipKzZo107/8y7/o6NGjPzq/L774QgMHDlRERITsdruGDRumb775ps7+faDuETtosJYvX67mzZtr9+7dGjt2rNLS0jRkyBDddttt+vTTTzVgwAANGzZMZ86cUXV1tVq2bKnVq1fr4MGDmjp1qp599lm9++67Vzx+VlaWVqxYoYULF+rAgQMaP368HnnkEW3fvr0ezxIwz2uvvaYZM2aoZcuWKioq0p49e6769TZt2jT9x3/8h3bu3KnCwkI9+OCDevXVV7Vy5UqtX79emzZt0rx587zjT58+rYyMDO3du1c5OTkKCgrS/fffr+rq6svOrbS0VH379tUtt9yivXv3Kjs7W8XFxXrwwQfr9N8J6pgHaIB69+7t6dWrl/fx+fPnPU2bNvUMGzbMu66oqMgjyZObm3vZY6Snp3uSk5O9j1NTUz333nuvx+PxeM6dO+dp0qSJZ+fOnT77jBgxwjN06NBaPBPgl2nOnDmehIQEj8dzda+3rVu3eiR5tmzZ4t2elZXlkeQ5evSod90f/vAHT1JS0hWf98SJEx5Jnv3793s8Ho/n2LFjHkmezz77zOPxeDwzZ870DBgwwGefwsJCjyTPoUOHany+8C/u2UGD1blzZ+8/BwcHq1mzZurUqZN33cWfCykpKZEkzZ8/X0uWLFFBQYHOnj2ryspKdenS5bLHPnLkiM6cOaO77rrLZ31lZaVuueWWWj4T4Jft57ze/vl1b7fb1aRJE7Vp08Zn3e7du72PDx8+rKlTp2rXrl365ptvvFd0CgoK1LFjx0vm8vnnn2vr1q2KiIi4ZNvRo0d144031uwk4VfEDhqsRo0a+Ty2WCw+6ywWi6Tv771ZtWqVJkyYoD/96U9yOp2KjIzUK6+8ol27dl322KdOnZIkrV+/Xr/61a98tvGbO0Dt+jmvtx++xi/334F/fotq8ODBSkhI0KJFixQXF6fq6mp17NhRlZWVV5zL4MGD9fLLL1+yrUWLFj/vxBAwiB38IuzYsUO33XabRo8e7V33YzcpJiYmKiwsTAUFBerdu3d9TBH4xaqr19vJkyd16NAhLVq0SL/97W8lSZ988smP7tO1a1e99957uv766xUSwp9IU/C/JH4R2rZtqxUrVmjjxo1q3bq1/vKXv2jPnj1q3br1ZcdHRkZqwoQJGj9+vKqrq9WrVy+VlZVpx44dslqtSk1NreczAMxVV6+36667Ts2aNdObb76pFi1aqKCgQM8888yP7pOenq5FixZp6NChmjRpkqKjo3XkyBGtWrVKb731loKDg2s0F/gXsYNfhD/84Q/67LPP9NBDD8lisWjo0KEaPXq096PplzNz5kzFxMQoKytL//jHPxQVFaWuXbvq2WefrceZA78MdfF6CwoK0qpVq/TUU0+pY8eOateunebOnas+ffpccZ+4uDjt2LFDkydP1oABA1RRUaGEhATdfffdCgriA8wNlcXj8Xj8PQkAAIC6QqYCAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7ABqkEydOKC0tTa1atVJYWJgcDoeSkpK0Y8cOf08NQIDht7EANEjJycmqrKzU8uXL1aZNGxUXFysnJ0cnT57099QABBiu7ABocEpLS/Xxxx/r5Zdf1p133qmEhAT95je/UWZmpn73u995xzzxxBOKiYmR1WpV37599fnnn0v6/qqQw+HQrFmzvMfcuXOnQkNDlZOT45dzAlB3iB0ADU5ERIQiIiL0/vvvq6Ki4rJjhgwZopKSEm3YsEH5+fnq2rWr+vXrp2+//VYxMTFasmSJpk2bpr1796q8vFzDhg3TmDFj1K9fv3o+GwB1jV89B9Agvffeexo5cqTOnj2rrl27qnfv3nr44YfVuXNnffLJJxo0aJBKSkoUFhbm3eeGG27QpEmTNGrUKElSenq6tmzZou7du2v//v3as2ePz3gAZiB2ADRY586d08cff6y8vDxt2LBBu3fv1ltvvaXTp0/rqaeeUnh4uM/4s2fPasKECXr55Ze9jzt27KjCwkLl5+erU6dO/jgNAHWM2AFgjCeeeEKbN2/W6NGjNW/ePG3btu2SMVFRUWrevLkk6YsvvtCtt96qqqoqrV27VoMHD67nGQOoD3waC4AxEhMT9f7776tr165yuVwKCQnR9ddff9mxlZWVeuSRR/TQQw+pXbt2euKJJ7R//37FxsbW76QB1Dmu7ABocE6ePKkhQ4bo8ccfV+fOnRUZGam9e/dq7NixGjRokN566y3dcccdKi8v1+zZs3XjjTfq+PHjWr9+ve6//351795dEydO1Jo1a/T5558rIiJCvXv3ls1m07p16/x9egBqGbEDoMGpqKjQtGnTtGnTJh09elRVVVWKj4/XkCFD9Oyzzyo8PFzl5eX693//d7333nvej5rfcccdysrK0tGjR3XXXXdp69at6tWrlyTpq6++0s0336yXXnpJaWlpfj5DALWJ2AEAAEbje3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAY7f8D0UrKA22XWSEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看性别的分布情况\n",
    "ax = sns.countplot(data=temp,x='Sex');\n",
    "ax.bar_label(ax.containers[0]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "5e34e764",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:04:41.775519Z",
     "start_time": "2022-04-27T07:04:41.392489Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 性别与存货的关系\n",
    "# Sex vs Survived\n",
    "ax = sns.countplot(data=temp,x='Sex',hue='Survived');\n",
    "ax.bar_label(ax.containers[0]);\n",
    "ax.bar_label(ax.containers[1]);\n",
    "plt.legend(title='Survived or Not', loc='upper right', labels=['No', 'Yes']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "c06de49d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:06:23.799820Z",
     "start_time": "2022-04-27T07:06:23.790261Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 3, 4, 2, 5, 8], dtype=int64)"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SibSp : 泰坦尼克号上的兄弟姐妹/配偶的数量\n",
    "temp.SibSp.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "fe8904b5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:06:57.624073Z",
     "start_time": "2022-04-27T07:06:57.131883Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oueeeU0JCAqepAACAJBPDTnXMnTtXHh4eGjVqlMrKyhQbG6uFCxc6+z09PbVmzRpNmDBBdrtdjRo1Unx8vFJTU02sGgAAuBObYRiG2UWYrbi4WIGBgSoqKlJAQMAl/T2T3jGhqrqTM+dBs0sAAOCq/dLv90WmP2cHAADgWiLsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAAS6tR2Bk0aJAKCwsvaS8uLtagQYN+bU0AAAC1pkZhZ9OmTSovL7+k/fz589qyZcuvLgoAAKC2eF3N4K+++sr556+//loOh8O5XllZqYyMDP3mN7+pveoAAAB+pasKO927d5fNZpPNZrvs6So/Pz/Nnz+/1ooDAAD4ta4q7Bw/flyGYahdu3bauXOnmjdv7uzz9vZWSEiIPD09a71IAACAmrqqsBMRESFJqqqquibFAAAA1LarCjs/deTIEW3cuFGnT5++JPxMmzbtVxcGAABQG2oUdt58801NmDBBzZo1U1hYmGw2m7PPZrMRdgAAgNuoUdh54YUXNGPGDE2ZMqW26wEAAKhVNXrOzg8//KD77ruvtmsBAACodTUKO/fdd5/Wrl1b27UAAADUuhqdxmrfvr2mTp2q7du3q2vXrmrQoIFL/xNPPFErxQEAAPxaNQo7b7zxhho3bqysrCxlZWW59NlsNsIOAABwGzUKO8ePH6/tOgAAAK6JGl2zAwAAUF/U6MjOuHHjfrb/7bffrlExAAAAta1GYeeHH35wWa+oqND+/ftVWFh42ReEAgAAmKVGYWfFihWXtFVVVWnChAm64YYbfnVRAAAAtaXWrtnx8PBQYmKi5s6dW1ubBAAA+NVq9QLlY8eO6cKFC7W5SQAAgF+lRqexEhMTXdYNw9CpU6f0j3/8Q/Hx8bVSGAAAQG2oUdjZs2ePy7qHh4eaN2+ul19++Rfv1AIAAKhLNQo7GzdurO06AAAArokahZ2Lzpw5o7y8PElSx44d1bx581opCgAAoLbU6ALl0tJSjRs3Ti1atNCAAQM0YMAAhYeHa/z48frxxx+rvZ1FixapW7duCggIUEBAgOx2uz777DNn//nz55WQkKCmTZuqcePGGjVqlAoKCly2kZ+fr2HDhqlhw4YKCQlRUlISF0kDAACnGoWdxMREZWVlafXq1SosLFRhYaE++eQTZWVl6amnnqr2dlq2bKmZM2cqJydHu3fv1qBBgzRixAgdOHBAkjR58mStXr1aH330kbKysnTy5Endc889zs9XVlZq2LBhKi8v17Zt27R06VItWbJE06ZNq8m0AACABdkMwzCu9kPNmjXTxx9/rIEDB7q0b9y4Uffff7/OnDlT44KCg4M1Z84c3XvvvWrevLmWLVume++9V5J06NAhde7cWdnZ2erbt68+++wz3XXXXTp58qRCQ0MlSenp6ZoyZYrOnDkjb2/van1ncXGxAgMDVVRUpICAgEv6eya9U+P51Ac5cx40uwQAAK7aL/1+X1SjIzs//vijM1z8VEhIyFWdxvqpyspKLV++XKWlpbLb7crJyVFFRYViYmKcYzp16qTWrVsrOztbkpSdna2uXbu61BIbG6vi4mLn0aHLKSsrU3FxscsCAACsqUZhx263KyUlRefPn3e2/ec//9Gf//xn2e32q9rWvn371LhxY/n4+Oixxx7TihUrFBkZKYfDIW9vbwUFBbmMDw0NlcPhkCQ5HI5LQtfF9YtjLictLU2BgYHOpVWrVldVMwAAqD9qdDfWvHnzNGTIELVs2VJRUVGSpL1798rHx0dr1669qm117NhRubm5Kioq0scff6z4+HhlZWXVpKxqS05OdnkwYnFxMYEHAACLqlHY6dq1q44cOaL33ntPhw4dkiSNGTNGcXFx8vPzu6pteXt7q3379pKknj17ateuXfrLX/6i0aNHq7y8XIWFhS5HdwoKChQWFiZJCgsL086dO122d/FurYtjLsfHx0c+Pj5XVScAAKifahR20tLSFBoaqkceecSl/e2339aZM2c0ZcqUGhdUVVWlsrIy9ezZUw0aNND69es1atQoSVJeXp7y8/Odp8rsdrtmzJih06dPKyQkRJKUmZmpgIAARUZG1rgGAABgHTW6Zuf1119Xp06dLmnv0qWL0tPTq72d5ORkbd68Wd9884327dun5ORkbdq0SXFxcQoMDNT48eOVmJiojRs3KicnRw8//LDsdrv69u0rSRo8eLAiIyM1duxY7d27V59//rmee+45JSQkcOQGAABIquGRHYfDoRYtWlzS3rx5c506dara2zl9+rQefPBBnTp1SoGBgerWrZs+//xz/fa3v5UkzZ07Vx4eHho1apTKysoUGxurhQsXOj/v6empNWvWaMKECbLb7WrUqJHi4+OVmppak2kBAAALqlHYadWqlbZu3aq2bdu6tG/dulXh4eHV3s5bb731s/2+vr5asGCBFixYcMUxERER+vTTT6v9nQAA4PpSo7DzyCOPaNKkSaqoqNCgQYMkSevXr9czzzxzVU9QBgAAuNZqFHaSkpL0/fff6w9/+IPKy8sl/fcozJQpU5ScnFyrBQIAAPwaNQo7NptNs2bN0tSpU3Xw4EH5+fmpQ4cOXBQMAADcTo3CzkWNGzdW7969a6sWAACAWlejW88BAADqC8IOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOTJWWlqbevXvL399fISEhGjlypPLy8lzGnD9/XgkJCWratKkaN26sUaNGqaCgwGXM+vXr1a9fP/n7+yssLExTpkzRhQsX6nIqAAA3RdiBqbKyspSQkKDt27crMzNTFRUVGjx4sEpLS51jJk+erNWrV+ujjz5SVlaWTp48qXvuucfZv3fvXt15550aMmSI9uzZow8++ECrVq3Ss88+a8aUAABuxmYYhmF2EWYrLi5WYGCgioqKFBAQcEl/z6R3TKiq7uTMedDsEpzOnDmjkJAQZWVlacCAASoqKlLz5s21bNky3XvvvZKkQ4cOqXPnzsrOzlbfvn31xz/+UZmZmdq1a5dzO6tXr9b999+v06dPy9/f36zpAACuoV/6/b6IIztwK0VFRZKk4OBgSVJOTo4qKioUExPjHNOpUye1bt1a2dnZkqSysjL5+vq6bMfPz0/nz59XTk5OHVUOAHBXhB24jaqqKk2aNEn9+/fXTTfdJElyOBzy9vZWUFCQy9jQ0FA5HA5JUmxsrLZt26b3339flZWV+ve//63U1FRJ0qlTp+p0DgAA90PYgdtISEjQ/v37tXz58qv63ODBgzVnzhw99thj8vHx0Y033qg777xTkuThwb/iAHC945cAbmHixIlas2aNNm7cqJYtWzrbw8LCVF5ersLCQpfxBQUFCgsLc64nJiaqsLBQ+fn5+u677zRixAhJUrt27eqkfgCA+yLswFSGYWjixIlasWKFNmzYoLZt27r09+zZUw0aNND69eudbXl5ecrPz5fdbncZa7PZFB4eLj8/P73//vtq1aqVevToUSfzAAC4Ly+zC8D1LSEhQcuWLdMnn3wif39/53U4gYGB8vPzU2BgoMaPH6/ExEQFBwcrICBAjz/+uOx2u/r27evczpw5czRkyBB5eHjo73//u2bOnKkPP/xQnp6eZk0NAOAmCDsw1aJFiyRJAwcOdGlfvHixHnroIUnS3Llz5eHhoVGjRqmsrEyxsbFauHChy/jPPvtMM2bMUFlZmaKiovTJJ59o6NChdTEFAICbI+zAVNV5zJOvr68WLFigBQsWXHHMhg0barMsAICFcM0OAACwNI7s4Ffh6dIAAHdn6pGd2noJZH5+voYNG6aGDRsqJCRESUlJvAQSAABIMjns1MZLICsrKzVs2DCVl5dr27ZtWrp0qZYsWaJp06aZMSUAAOBmTD2NlZGR4bK+ZMkShYSEKCcnx/kSyLfeekvLli3ToEGDJP33Lp3OnTtr+/bt6tu3r9auXauvv/5a69atU2hoqLp3767nn39eU6ZM0fTp0+Xt7W3G1AAAgJtwqwuUa/ISyOzsbHXt2lWhoaHOMbGxsSouLtaBAwcu+z1lZWUqLi52WQAAgDW5Tdip6UsgHQ6HS9C52H+x73LS0tIUGBjoXFq1alXLswEAAO7CbcJOTV8CWRPJyckqKipyLidOnLjm3wkAAMzhFreeX3wJ5ObNm6/4EsifHt356Usgw8LCtHPnTpftXbxb66cvivwpHx8f+fj41PIsAACAOzL1yE5tvATSbrdr3759On36tHNMZmamAgICFBkZWTcTAQAAbsvUIzu18RLIwYMHKzIyUmPHjtXs2bPlcDj03HPPKSEhgaM3AADA3LBTGy+B9PT01Jo1azRhwgTZ7XY1atRI8fHxSk1NratpAAAAN2Zq2Kmtl0BGRETo008/rc3SAACARbjN3VgAAADXAmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYmqlhZ/PmzRo+fLjCw8Nls9m0cuVKl37DMDRt2jS1aNFCfn5+iomJ0ZEjR1zGnD17VnFxcQoICFBQUJDGjx+vkpKSOpwFAABwZ6aGndLSUkVFRWnBggWX7Z89e7ZeffVVpaena8eOHWrUqJFiY2N1/vx555i4uDgdOHBAmZmZWrNmjTZv3qxHH320rqYAAADcnJeZXz506FANHTr0sn2GYWjevHl67rnnNGLECEnSO++8o9DQUK1cuVIPPPCADh48qIyMDO3atUu9evWSJM2fP1933nmnXnrpJYWHh9fZXAAAgHty22t2jh8/LofDoZiYGGdbYGCgoqOjlZ2dLUnKzs5WUFCQM+hIUkxMjDw8PLRjx44rbrusrEzFxcUuCwAAsCa3DTsOh0OSFBoa6tIeGhrq7HM4HAoJCXHp9/LyUnBwsHPM5aSlpSkwMNC5tGrVqparBwAA7sJtw861lJycrKKiIudy4sQJs0sCAADXiNuGnbCwMElSQUGBS3tBQYGzLywsTKdPn3bpv3Dhgs6ePescczk+Pj4KCAhwWQAAgDW5bdhp27atwsLCtH79emdbcXGxduzYIbvdLkmy2+0qLCxUTk6Oc8yGDRtUVVWl6OjoOq8ZAAC4H1PvxiopKdHRo0ed68ePH1dubq6Cg4PVunVrTZo0SS+88II6dOigtm3baurUqQoPD9fIkSMlSZ07d9aQIUP0yCOPKD09XRUVFZo4caIeeOAB7sQCAACSTA47u3fv1u233+5cT0xMlCTFx8dryZIleuaZZ1RaWqpHH31UhYWFuuWWW5SRkSFfX1/nZ9577z1NnDhRd9xxhzw8PDRq1Ci9+uqrdT4XAADgnkwNOwMHDpRhGFfst9lsSk1NVWpq6hXHBAcHa9myZdeiPAAAYAFue80OAABAbSDsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAPVEWlqaevfuLX9/f4WEhGjkyJHKy8tzGeNwODR27FiFhYWpUaNG6tGjh/72t7+ZVDEAuAfCDlBPZGVlKSEhQdu3b1dmZqYqKio0ePBglZaWOsc8+OCDysvL06pVq7Rv3z7dc889uv/++7Vnzx4TKwcAc3mZXQCA6snIyHBZX7JkiUJCQpSTk6MBAwZIkrZt26ZFixapT58+kqTnnntOc+fOVU5Ojm6++eY6rxkA3AFHdoB6qqioSJIUHBzsbOvXr58++OADnT17VlVVVVq+fLnOnz+vgQMHmlQlAJiPIztAPVRVVaVJkyapf//+uummm5ztH374oUaPHq2mTZvKy8tLDRs21IoVK9S+fXsTqwUAcxF2gHooISFB+/fv1xdffOHSPnXqVBUWFmrdunVq1qyZVq5cqfvvv19btmxR165dTaoWAMzFaSygnpk4caLWrFmjjRs3qmXLls72Y8eO6bXXXtPbb7+tO+64Q1FRUUpJSVGvXr20YMECEyuGmTZv3qzhw4crPDxcNptNK1eudOkvKSnRxIkT1bJlS/n5+SkyMlLp6enmFAtcI4QdoJ4wDEMTJ07UihUrtGHDBrVt29al/8cff5QkeXi4/mft6empqqqqOqvTDL/0gy5JBw8e1O9+9zsFBgaqUaNG6t27t/Lz8+u+2DpWWlqqqKioKwbexMREZWRk6N1339XBgwc1adIkTZw4UatWrarjSoFrh7AD1BMJCQl69913tWzZMvn7+8vhcMjhcOg///mPJKlTp05q3769fv/732vnzp06duyYXn75ZWVmZmrkyJHmFn+N/dIP+rFjx3TLLbeoU6dO2rRpk7766itNnTpVvr6+dVxp3Rs6dKheeOEF3X333Zft37Ztm+Lj4zVw4EC1adNGjz76qKKiorRz5846rhS4drhmB6gnFi1aJEmX3Fm1ePFiPfTQQ2rQoIE+/fRTPfvssxo+fLhKSkrUvn17LV26VHfeeacJFdedoUOHaujQoVfs/9Of/qQ777xTs2fPdrbdcMMNdVGa2+vXr59WrVqlcePGKTw8XJs2bdLhw4c1d+5cs0sDag1hB6gnDMP4xTEdOnTgicn/R1VVlf7xj3/omWeeUWxsrPbs2aO2bdsqOTnZ8ke8qmP+/Pl69NFH1bJlS3l5ecnDw0Nvvvmm89lNgBVwGguApZ0+fVolJSWaOXOmhgwZorVr1+ruu+/WPffco6ysLLPLM938+fO1fft2rVq1Sjk5OXr55ZeVkJCgdevWmV0aUGs4sgPA0i5enD1ixAhNnjxZktS9e3dt27ZN6enpuu2228wsz1T/+c9/9Mc//lErVqzQsGHDJEndunVTbm6uXnrpJcXExJhcIVA7CDvANdAz6R2zS7jmcuY8aHYJ1dKsWTN5eXkpMjLSpb1z586XPKfoelNRUaGKiorr8g4+XJ02bdroX//61yXtf/jDH+rFoy04jQXA0ry9vdW7d+9L3hB/+PBhRUREmFRV3SkpKVFubq5yc3MlScePH1dubq7y8/MVEBCg2267TUlJSdq0aZOOHz+uJUuW6J133rni3VtWNn36dNlsNpelU6dOZpflFnbt2qVTp045l8zMTEnSfffdZ3Jl1cORHQD1XklJiY4ePepcv/iDHhwcrNatWyspKUmjR4/WgAEDdPvttysjI0OrV6/Wpk2bzCu6juzevVu33367cz0xMVGSFB8fryVLlmj58uVKTk5WXFyczp49q4iICM2YMUOPPfaYWSWbqkuXLi7XK3l58TMpSc2bN3dZnzlzpm644YZ6cxqYf4oA6r1f+kG/++67lZ6errS0ND3xxBPq2LGj/va3v+mWW24xq+Q6M3DgwJ+9ky8sLEyLFy+uw4rcm5eXl8LCwswuw62Vl5fr3XffVWJiomw2m9nlVAthB0C990s/6JI0btw4jRs3ro4qQn115MgRhYeHy9fXV3a7XWlpaWrdurXZZbmVlStXqrCwUA899JDZpVQbYQcA3AQXtpsrOjpaS5YsUceOHXXq1Cn9+c9/1q233qr9+/fL39/f7PLcxltvvaWhQ4cqPDzc7FKqjbADoE7xgw539dOncHfr1k3R0dGKiIjQhx9+qPHjx5tYmfv417/+pXXr1unvf/+72aVcFe7GAgDgMoKCgnTjjTe6XPx+vVu8eLFCQkKcz2WqLwg7AABcRklJiY4dO6YWLVqYXYpbqKqq0uLFixUfH1/v7lIj7AAAIOnpp59WVlaWvvnmG23btk133323PD09NWbMGLNLcwvr1q1Tfn5+vbzQv35FMwAArpFvv/1WY8aM0ffff6/mzZvrlltu0fbt2y95xsz1avDgwdV6IbE7IuwAACBp+fLlZpeAa8QyYWfBggWaM2eOHA6HoqKiNH/+fPXp08fssgAAuKa4w/GXWSLsfPDBB0pMTFR6erqio6M1b948xcbGKi8vTyEhIWaXBwD4lfhBx69hiQuUX3nlFT3yyCN6+OGHFRkZqfT0dDVs2FBvv/222aUBAACT1fsjO+Xl5crJyVFycrKzzcPDQzExMcrOzr7sZ8rKylRWVuZcLyoqkiQVFxdfdnxl2X9qsWL3c6V5Vwf75vKsvl8k9s3PYd9cGfvmytg3V3alfXOx/RcvnDbquX//+9+GJGPbtm0u7UlJSUafPn0u+5mUlBRDEgsLCwsLC4sFlhMnTvxsVqj3R3ZqIjk52flWZOm/D0o6e/asmjZtavobXIuLi9WqVSudOHFCAQEBptbibtg3V8a+uTL2zZWxby6P/XJl7rZvDMPQuXPnfvE9XfU+7DRr1kyenp4qKChwaS8oKFBYWNhlP+Pj4yMfHx+XtqCgoGtVYo0EBAS4xb9I7oh9c2Xsmytj31wZ++by2C9X5k77JjAw8BfH1PsLlL29vdWzZ0+tX7/e2VZVVaX169fLbrebWBkAAHAH9f7IjiQlJiYqPj5evXr1Up8+fTRv3jyVlpbq4YcfNrs0AABgMkuEndGjR+vMmTOaNm2aHA6HunfvroyMDIWGhppd2lXz8fFRSkrKJafZwL75OeybK2PfXBn75vLYL1dWX/eNzTDq6YsuAAAAqqHeX7MDAADwcwg7AADA0gg7AADA0gg7AADA0gg7bmbBggVq06aNfH19FR0drZ07d5pdkuk2b96s4cOHKzw8XDabTStXrjS7JLeQlpam3r17y9/fXyEhIRo5cqTy8vLMLsstLFq0SN26dXM++Mxut+uzzz4zuyy3NHPmTNlsNk2aNMnsUkw3ffp02Ww2l6VTp05ml+UWKisrNXXqVLVt21Z+fn664YYb9Pzzz//yO6ncBGHHjXzwwQdKTExUSkqKvvzyS0VFRSk2NlanT582uzRTlZaWKioqSgsWLDC7FLeSlZWlhIQEbd++XZmZmaqoqNDgwYNVWlpqdmmma9mypWbOnKmcnBzt3r1bgwYN0ogRI3TgwAGzS3Mru3bt0uuvv65u3bqZXYrb6NKli06dOuVcvvjiC7NLcguzZs3SokWL9Nprr+ngwYOaNWuWZs+erfnz55tdWrVw67kbiY6OVu/evfXaa69J+u+ToFu1aqXHH39czz77rMnVuQebzaYVK1Zo5MiRZpfids6cOaOQkBBlZWVpwIABZpfjdoKDgzVnzhyNHz/e7FLcQklJiXr06KGFCxfqhRdeUPfu3TVv3jyzyzLV9OnTtXLlSuXm5ppditu56667FBoaqrfeesvZNmrUKPn5+endd981sbLq4ciOmygvL1dOTo5iYmKcbR4eHoqJiVF2draJlaG+KCoqkvTfH3X8r8rKSi1fvlylpaW8QuYnEhISNGzYMJe/cyAdOXJE4eHhateuneLi4pSfn292SW6hX79+Wr9+vQ4fPixJ2rt3r7744gsNHTrU5MqqxxJPULaC7777TpWVlZc89Tk0NFSHDh0yqSrUF1VVVZo0aZL69++vm266yexy3MK+fftkt9t1/vx5NW7cWCtWrFBkZKTZZbmF5cuX68svv9SuXbvMLsWtREdHa8mSJerYsaNOnTqlP//5z7r11lu1f/9++fv7m12eqZ599lkVFxerU6dO8vT0VGVlpWbMmKG4uDizS6sWwg5gAQkJCdq/fz/XF/xEx44dlZubq6KiIn388ceKj49XVlbWdR94Tpw4oSeffFKZmZny9fU1uxy38tOjFN26dVN0dLQiIiL04YcfXvenPz/88EO99957WrZsmbp06aLc3FxNmjRJ4eHhio+PN7u8X0TYcRPNmjWTp6enCgoKXNoLCgoUFhZmUlWoDyZOnKg1a9Zo8+bNatmypdnluA1vb2+1b99ektSzZ0/t2rVLf/nLX/T666+bXJm5cnJydPr0afXo0cPZVllZqc2bN+u1115TWVmZPD09TazQfQQFBenGG2/U0aNHzS7FdElJSXr22Wf1wAMPSJK6du2qf/3rX0pLS6sXYYdrdtyEt7e3evbsqfXr1zvbqqqqtH79eq4zwGUZhqGJEydqxYoV2rBhg9q2bWt2SW6tqqpKZWVlZpdhujvuuEP79u1Tbm6uc+nVq5fi4uKUm5tL0PmJkpISHTt2TC1atDC7FNP9+OOP8vBwjQyenp6qqqoyqaKrw5EdN5KYmKj4+Hj16tVLffr00bx581RaWqqHH37Y7NJMVVJS4vJ/VsePH1dubq6Cg4PVunVrEyszV0JCgpYtW6ZPPvlE/v7+cjgckqTAwED5+fmZXJ25kpOTNXToULVu3Vrnzp3TsmXLtGnTJn3++edml2Y6f3//S67ratSokZo2bXrdX+/19NNPa/jw4YqIiNDJkyeVkpIiT09PjRkzxuzSTDd8+HDNmDFDrVu3VpcuXbRnzx698sorGjdunNmlVY8BtzJ//nyjdevWhre3t9GnTx9j+/btZpdkuo0bNxqSLlni4+PNLs1Ul9snkozFixebXZrpxo0bZ0RERBje3t5G8+bNjTvuuMNYu3at2WW5rdtuu8148sknzS7DdKNHjzZatGhheHt7G7/5zW+M0aNHG0ePHjW7LLdQXFxsPPnkk0br1q0NX19fo127dsaf/vQno6yszOzSqoXn7AAAAEvjmh0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0A19Q333wjm82m3Nxcs0txOnTokPr27StfX1917969xtux2WxauXKlpOrNc9OmTbLZbCosLJQkLVmyREFBQTX+fgDVQ9gBLO6hhx6SzWbTzJkzXdpXrlwpm81mUlXmSklJUaNGjZSXl+fy8t2fOnPmjCZMmKDWrVvLx8dHYWFhio2N1datW51jTp06paFDh9a4jtGjR+vw4cM1/jyA6uFFoMB1wNfXV7NmzdLvf/97NWnSxOxyakV5ebm8vb1r9Nljx45p2LBhioiIuOKYUaNGqby8XEuXLlW7du1UUFCg9evX6/vvv3eOCQsLq9H3X+Tn53fdv7QVqAsc2QGuAzExMQoLC1NaWtoVx0yfPv2SUzrz5s1TmzZtnOsPPfSQRo4cqRdffFGhoaEKCgpSamqqLly4oKSkJAUHB6tly5ZavHjxJds/dOiQ+vXrJ19fX910003Kyspy6d+/f7+GDh2qxo0bKzQ0VGPHjtV3333n7B84cKAmTpyoSZMmqVmzZoqNjb3sPKqqqpSamqqWLVvKx8dH3bt3V0ZGhrPfZrMpJydHqampstlsmj59+iXbKCws1JYtWzRr1izdfvvtioiIUJ8+fZScnKzf/e53Ltu6eBqruvP8qf97GuviP4P/+Z//UZs2bRQYGKgHHnhA586dc445d+6c4uLi1KhRI7Vo0UJz587VwIEDNWnSJOeYhQsXqkOHDvL19VVoaKjuvffeK9YAXA8IO8B1wNPTUy+++KLmz5+vb7/99ldta8OGDTp58qQ2b96sV155RSkpKbrrrrvUpEkT7dixQ4899ph+//vfX/I9SUlJeuqpp7Rnzx7Z7XYNHz7ceZSksLBQgwYN0s0336zdu3crIyNDBQUFuv/++122sXTpUnl7e2vr1q1KT0+/bH1/+ctf9PLLL+ull17SV199pdjYWP3ud7/TkSNHJP331FOXLl301FNP6dSpU3r66acv2Ubjxo3VuHFjrVy5UmVlZVe1f35untVx7NgxrVy5UmvWrNGaNWuUlZXlcgoyMTFRW7du1apVq5SZmaktW7boyy+/dPbv3r1bTzzxhFJTU5WXl6eMjAwNGDDgquYAWI7Zr10HcG3Fx8cbI0aMMAzDMPr27WuMGzfOMAzDWLFihfHTvwJSUlKMqKgol8/OnTvXiIiIcNlWRESEUVlZ6Wzr2LGjceuttzrXL1y4YDRq1Mh4//33DcMwjOPHjxuSjJkzZzrHVFRUGC1btjRmzZplGIZhPP/888bgwYNdvvvEiROGJCMvL88wDMO47bbbjJtvvvkX5xseHm7MmDHDpa13797GH/7wB+d6VFSUkZKS8rPb+fjjj40mTZoYvr6+Rr9+/Yzk5GRj7969LmMkGStWrKj2PDdu3GhIMn744QfDMAxj8eLFRmBgoHN8SkqK0bBhQ6O4uNjZlpSUZERHRxuGYRjFxcVGgwYNjI8++sjZX1hYaDRs2NB48sknDcMwjL/97W9GQECAyzaA6x1HdoDryKxZs7R06VIdPHiwxtvo0qWLPDz+96+O0NBQde3a1bnu6emppk2b6vTp0y6fs9vtzj97eXmpV69ezjr27t2rjRs3Oo+oNG7cWJ06dZL03yMdF/Xs2fNnaysuLtbJkyfVv39/l/b+/ftf9ZxHjRqlkydPatWqVRoyZIg2bdqkHj16aMmSJT/7uZ+bZ3W0adNG/v7+zvUWLVo49+U///lPVVRUqE+fPs7+wMBAdezY0bn+29/+VhEREWrXrp3Gjh2r9957Tz/++GO1vx+wIsIOcB0ZMGCAYmNjlZycfEmfh4eHDMNwaauoqLhkXIMGDVzWbTbbZduqqqqqXVdJSYmGDx+u3Nxcl+XIkSMup2AaNWpU7W3WBl9fX/32t7/V1KlTtW3bNj300ENKSUm5pt/5a/elv7+/vvzyS73//vtq0aKFpk2bpqioKOft7sD1iLADXGdmzpyp1atXKzs726W9efPmcjgcLoGnNp+Ns337duefL1y4oJycHHXu3FmS1KNHDx04cEBt2rRR+/btXZarCTgBAQEKDw93uT1ckrZu3arIyMhfPYfIyEiVlpb+7Jifm+ev1a5dOzVo0EC7du1ythUVFV1y+7qXl5diYmI0e/ZsffXVV/rmm2+0YcOGWqkBqI+49Ry4znTt2lVxcXF69dVXXdoHDhyoM2fOaPbs2br33nuVkZGhzz77TAEBAbXyvQsWLFCHDh3UuXNnzZ07Vz/88IPGjRsnSUpISNCbb76pMWPG6JlnnlFwcLCOHj2q5cuX669//as8PT2r/T1JSUlKSUnRDTfcoO7du2vx4sXKzc3Ve++9V+1tfP/997rvvvs0btw4devWTf7+/tq9e7dmz56tESNG1Hiev5a/v7/i4+Odd76FhIQoJSVFHh4ezmcmrVmzRv/85z81YMAANWnSRJ9++qmqqqpcTnUB1xuO7ADXodTU1EtOjXTu3FkLFy7UggULFBUVpZ07d172TqWamjlzpmbOnKmoqCh98cUXWrVqlZo1ayZJzqMxlZWVGjx4sLp27apJkyYpKCjI5fqg6njiiSeUmJiop556Sl27dlVGRoZWrVqlDh06VHsbjRs3VnR0tObOnasBAwbopptu0tSpU/XII4/otddeq/E8a8Mrr7wiu92uu+66SzExMerfv786d+4sX19fSVJQUJD+/ve/a9CgQercubPS09P1/vvvq0uXLrVWA1Df2Iz/e5IeAFBvlJaW6je/+Y1efvlljR8/3uxyALfEaSwAqEf27NmjQ4cOqU+fPioqKlJqaqok/eLpNeB6RtgBgHrmpZdeUl5enry9vdWzZ09t2bKlVk+VAVbDaSwAAGBpXKAMAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAs7f8DQ54aIGo6cYcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  泰坦尼克号上的兄弟姐妹/配偶的数量 分布\n",
    "ax = sns.countplot(data=temp,x='SibSp');\n",
    "ax.bar_label(ax.containers[0]);\n",
    "ax.set_xlabel('Number of Siblings');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "84614c65",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:08:12.349022Z",
     "start_time": "2022-04-27T07:08:11.664274Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Number of Siblings vs Survived\n",
    "# 兄弟姐妹、配偶数量与是否存货的关系\n",
    "ax = sns.countplot(data=temp,x='SibSp',hue='Survived');\n",
    "ax.bar_label(ax.containers[0]);\n",
    "ax.bar_label(ax.containers[1]);\n",
    "plt.legend(title='Survived or Not', loc='upper right', labels=['No', 'Yes']);\n",
    "plt.title('Effect of Number of Siblings On Survival')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "efc3835d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:15:06.142386Z",
     "start_time": "2022-04-27T07:15:06.131818Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 5, 3, 4, 6], dtype=int64)"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Parch: 泰坦尼克号上的父母/孩子人数\n",
    "\n",
    "temp.Parch.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "2160297c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:15:13.966876Z",
     "start_time": "2022-04-27T07:15:13.493318Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(data=temp,x='Parch');\n",
    "ax.bar_label(ax.containers[0]);\n",
    "ax.set_xlabel(' # Parents / Children Aboard The Titanic');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "8a19da68",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:15:24.339568Z",
     "start_time": "2022-04-27T07:15:23.576825Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Parch vs Survived\n",
    "plt.figure(figsize=(10,6))\n",
    "ax = sns.countplot(data=temp,x='Parch',hue='Survived');\n",
    "ax.bar_label(ax.containers[0]);\n",
    "ax.bar_label(ax.containers[1]);\n",
    "plt.legend(title='Survived or Not', loc='upper right', labels=['No', 'Yes']);\n",
    "plt.title('Effect of # Parents / Children Aboard The Titanic On Survival')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "45c2a400",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:15:39.886618Z",
     "start_time": "2022-04-27T07:15:39.877115Z"
    }
   },
   "outputs": [],
   "source": [
    "temp['Family Size'] = temp['SibSp']+temp['Parch'] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "c346914e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:15:48.066108Z",
     "start_time": "2022-04-27T07:15:47.552194Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(data=temp, x='Family Size')\n",
    "ax.bar_label(ax.containers[0])\n",
    "ax.set_title('Family Size');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "3e8bacec",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:15:59.815610Z",
     "start_time": "2022-04-27T07:15:59.008567Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Family Size vs Survived\n",
    "plt.figure(figsize=(10,6))\n",
    "ax = sns.countplot(data=temp,x='Family Size',hue='Survived');\n",
    "ax.bar_label(ax.containers[0]);\n",
    "ax.bar_label(ax.containers[1]);\n",
    "plt.legend(title='Survived or Not', loc='upper right', labels=['No', 'Yes']);\n",
    "plt.title('Family Size vs Survival')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "caa3896c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:19:42.311120Z",
     "start_time": "2022-04-27T07:19:42.299901Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['S', 'C', 'Q', nan], dtype=object)"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Embarked 登船港口\n",
    "temp.Embarked.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "805027c9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:19:52.248487Z",
     "start_time": "2022-04-27T07:19:51.909272Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(data=temp,x='Embarked');\n",
    "ax.bar_label(ax.containers[0]);\n",
    "ax.set_xlabel(' Port of Embarkation');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "fc9d3805",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:20:00.669076Z",
     "start_time": "2022-04-27T07:20:00.150048Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Port of Embarkation vs Survived\n",
    "plt.figure(figsize=(10,6))\n",
    "ax = sns.countplot(data=temp,x='Embarked',hue='Survived');\n",
    "ax.bar_label(ax.containers[0]);\n",
    "ax.bar_label(ax.containers[1]);\n",
    "plt.legend(title='Survived or Not', loc='upper right', labels=['No', 'Yes']);\n",
    "plt.title('Port of Embarkation Effect On Survival')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "65348034",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:22:03.841104Z",
     "start_time": "2022-04-27T07:22:03.830380Z"
    }
   },
   "outputs": [],
   "source": [
    "# EDA结论\n",
    "# EDA Analysis Results\n",
    "# Based On The Dataset Only 40% People Were Able To Survived The Disaster.\n",
    "# Mostly Childrens Are Being Rescued.\n",
    "# People Above Age 20 had a chance of 35% of Being Survived From The Disaster.\n",
    "# People Paid High Fare and Class Means VIPs Are Given Priority For Rescued.\n",
    "# Out of Male and Females, Almost 75% Femals Survived The Disaster.\n",
    "# People Traveling Alone or With A Smaller Family Size Upto 2 children had high chances of survival.\n",
    "# Family Size Under 5 Had Higher Chance of Survival On Titanic Disaster.\n",
    "# People Traveling Alone had approx 43% Chances of Survival.\n",
    "# Family with Size 5+ Had Lesser Chance of Complete Survival On Titanic Disaster.\n",
    "# 根据数据集，只有 40% 的人能够在灾难中幸存下来。\n",
    "# 大多数儿童正在获救。\n",
    "# 20 岁以上的人有 35% 的几率从灾难中幸存下来。\n",
    "# 人们支付高票价和等级意味着 VIP 优先获救。\n",
    "# 在男性和女性中，几乎 75% 的女性在灾难中幸存下来。\n",
    "# 独自旅行或与多达 2 名儿童一起旅行的人有很高的生存机会。\n",
    "# 5个成员以下的家庭在泰坦尼克号灾难中的生存几率更高。\n",
    "# 独自旅行的人有大约 43% 的生存机会。\n",
    "# 5个成员以上的家庭在泰坦尼克号灾难中完全生存的机会较小。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "decc0153",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "354fb87b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:34:54.433827Z",
     "start_time": "2022-04-27T07:34:54.418109Z"
    }
   },
   "outputs": [],
   "source": [
    "def clean_data(data):\n",
    "    # Let's Start By Dropping `PassengerID` `Name` `Ticket` `Cabin` \n",
    "    # 删除关联性不大的`PassengerID` `Name` `Ticket` `Cabin`列\n",
    "    data = data.drop(['PassengerId','Name','Ticket','Cabin'],axis=1)\n",
    "    \n",
    "    # Converting Age Into Four Different Classes 'Children', 'Teenage','Adult', 'old'\n",
    "    # 根据年龄分为四个不同的类别“儿童”、“青少年”、“成人”、“老年”\n",
    "    # https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.cut.html    \n",
    "    data['Age'] = pd.cut(data['Age'], bins=[0,12,20,40,120], labels=['Children','Teenage','Adult','Elder'])\n",
    "    \n",
    "    # Converting Fare Into Four Distinct Categories 'Low_fare','median_fare','Average_fare','high_fare'\n",
    "    # 根据票价转换为四个不同的类别 'Low_fare'、'median_fare'、'Average_fare'、'high_fare'\n",
    "    data['Fare'] = pd.cut(data['Fare'], bins=[0,7.91,14.45,31,120], labels=['Low_fare','median_fare','Average_fare','high_fare'])\n",
    "    \n",
    "    #  Getting OneHotEncoding For Categorical Columns ['Age','Fare','Sex','Embarked']\n",
    "    # get_dummies函数进行one-hot编码， 比如Sex分为female和male，那么分为Sex_female和Sex_male，（1，0）表示Female\n",
    "    # https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.get_dummies.html\n",
    "    # data = pd.get_dummies(data, columns = [\"Sex\",\"Age\",\"Embarked\",\"Fare\"])\n",
    "    data = pd.get_dummies(data, columns = [\"Sex\",\"Age\",\"Embarked\",\"Fare\"], dtype=int)\n",
    "    \n",
    "    \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "7ad40587-7ab6-49ea-ad30-84451e567136",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
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       "      <td>112053</td>\n",
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       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
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       "      <td>29</td>\n",
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       "      <td>W./C. 6607</td>\n",
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       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
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       "      <td>26</td>\n",
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       "      <td>Dooley, Mr. Patrick</td>\n",
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       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex  Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male   22      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
       "2                               Heikkinen, Miss. Laina  female   26      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                             Allen, Mr. William Henry    male   35      0   \n",
       "..                                                 ...     ...  ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male   27      0   \n",
       "887                       Graham, Miss. Margaret Edith  female   19      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   29      1   \n",
       "889                              Behr, Mr. Karl Howell    male   26      0   \n",
       "890                                Dooley, Mr. Patrick    male   32      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 248,
     "metadata": {},
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    }
   ],
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    "df_labeled"
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  },
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     "start_time": "2022-04-27T07:35:24.980770Z"
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      "text/plain": [
       "     Survived  Pclass  SibSp  Parch  Sex_female  Sex_male  Age_Children  \\\n",
       "0           0       3      1      0           0         1             0   \n",
       "1           1       1      1      0           1         0             0   \n",
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       "..        ...     ...    ...    ...         ...       ...           ...   \n",
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       "889         1       1      0      0           0         1             0   \n",
       "890         0       3      0      0           0         1             0   \n",
       "\n",
       "     Age_Teenage  Age_Adult  Age_Elder  Embarked_C  Embarked_Q  Embarked_S  \\\n",
       "0              0          1          0           0           0           1   \n",
       "1              0          1          0           1           0           0   \n",
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       "..           ...        ...        ...         ...         ...         ...   \n",
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       "\n",
       "     Fare_Low_fare  Fare_median_fare  Fare_Average_fare  Fare_high_fare  \n",
       "0                1                 0                  0               0  \n",
       "1                0                 0                  0               1  \n",
       "2                0                 1                  0               0  \n",
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       "4                0                 1                  0               0  \n",
       "..             ...               ...                ...             ...  \n",
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       "889              0                 0                  1               0  \n",
       "890              1                 0                  0               0  \n",
       "\n",
       "[891 rows x 17 columns]"
      ]
     },
     "execution_count": 249,
     "metadata": {},
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   ],
   "source": [
    "df_labeled = clean_data(df_labeled)\n",
    "df_labeled"
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  {
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     "end_time": "2022-04-27T07:42:22.475457Z",
     "start_time": "2022-04-27T07:42:22.463171Z"
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       "      <th>Embarked_S</th>\n",
       "      <th>Fare_Low_fare</th>\n",
       "      <th>Fare_median_fare</th>\n",
       "      <th>Fare_Average_fare</th>\n",
       "      <th>Fare_high_fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  SibSp  Parch  Sex_female  Sex_male  Age_Children  Age_Teenage  \\\n",
       "0         3      1      0           0         1             0            0   \n",
       "1         1      1      0           1         0             0            0   \n",
       "2         3      0      0           1         0             0            0   \n",
       "3         1      1      0           1         0             0            0   \n",
       "4         3      0      0           0         1             0            0   \n",
       "..      ...    ...    ...         ...       ...           ...          ...   \n",
       "886       2      0      0           0         1             0            0   \n",
       "887       1      0      0           1         0             0            1   \n",
       "888       3      1      2           1         0             0            0   \n",
       "889       1      0      0           0         1             0            0   \n",
       "890       3      0      0           0         1             0            0   \n",
       "\n",
       "     Age_Adult  Age_Elder  Embarked_C  Embarked_Q  Embarked_S  Fare_Low_fare  \\\n",
       "0            1          0           0           0           1              1   \n",
       "1            1          0           1           0           0              0   \n",
       "2            1          0           0           0           1              0   \n",
       "3            1          0           0           0           1              0   \n",
       "4            1          0           0           0           1              0   \n",
       "..         ...        ...         ...         ...         ...            ...   \n",
       "886          1          0           0           0           1              0   \n",
       "887          0          0           0           0           1              0   \n",
       "888          1          0           0           0           1              0   \n",
       "889          1          0           1           0           0              0   \n",
       "890          1          0           0           1           0              1   \n",
       "\n",
       "     Fare_median_fare  Fare_Average_fare  Fare_high_fare  \n",
       "0                   0                  0               0  \n",
       "1                   0                  0               1  \n",
       "2                   1                  0               0  \n",
       "3                   0                  0               1  \n",
       "4                   1                  0               0  \n",
       "..                ...                ...             ...  \n",
       "886                 1                  0               0  \n",
       "887                 0                  1               0  \n",
       "888                 0                  1               0  \n",
       "889                 0                  1               0  \n",
       "890                 0                  0               0  \n",
       "\n",
       "[891 rows x 16 columns]"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# X： 去除df_labeled的‘Survived’列\n",
    "# Y： 保留df_labeled的‘Survived’列\n",
    "X = df_labeled.drop('Survived',axis=1)\n",
    "y = df_labeled['Survived']\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "bf9e5ab0-21bb-4267-8eae-17f7d25e01ac",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      0\n",
       "      ..\n",
       "886    0\n",
       "887    1\n",
       "888    0\n",
       "889    1\n",
       "890    0\n",
       "Name: Survived, Length: 891, dtype: int64"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ecc81499",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Age_Children</th>\n",
       "      <th>Age_Teenage</th>\n",
       "      <th>Age_Adult</th>\n",
       "      <th>Age_Elder</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Fare_Low_fare</th>\n",
       "      <th>Fare_median_fare</th>\n",
       "      <th>Fare_Average_fare</th>\n",
       "      <th>Fare_high_fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.131900</td>\n",
       "      <td>0.131900</td>\n",
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       "      <td>0.100910</td>\n",
       "      <td>-0.301990</td>\n",
       "      <td>-0.243292</td>\n",
       "      <td>0.221009</td>\n",
       "      <td>0.081720</td>\n",
       "      <td>0.450238</td>\n",
       "      <td>0.200349</td>\n",
       "      <td>-0.042044</td>\n",
       "      <td>-0.453875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>0.083081</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.114631</td>\n",
       "      <td>-0.114631</td>\n",
       "      <td>0.358489</td>\n",
       "      <td>-0.003250</td>\n",
       "      <td>-0.131867</td>\n",
       "      <td>-0.080458</td>\n",
       "      <td>-0.059528</td>\n",
       "      <td>-0.026354</td>\n",
       "      <td>0.070941</td>\n",
       "      <td>-0.232991</td>\n",
       "      <td>-0.202833</td>\n",
       "      <td>0.121717</td>\n",
       "      <td>0.336510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0.018443</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.245489</td>\n",
       "      <td>-0.245489</td>\n",
       "      <td>0.352285</td>\n",
       "      <td>-0.014161</td>\n",
       "      <td>-0.187289</td>\n",
       "      <td>-0.009268</td>\n",
       "      <td>-0.011069</td>\n",
       "      <td>-0.081228</td>\n",
       "      <td>0.063036</td>\n",
       "      <td>-0.244930</td>\n",
       "      <td>-0.203853</td>\n",
       "      <td>0.161366</td>\n",
       "      <td>0.253944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_female</th>\n",
       "      <td>-0.131900</td>\n",
       "      <td>0.114631</td>\n",
       "      <td>0.245489</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.075254</td>\n",
       "      <td>0.041846</td>\n",
       "      <td>-0.045831</td>\n",
       "      <td>-0.026242</td>\n",
       "      <td>0.082853</td>\n",
       "      <td>0.074115</td>\n",
       "      <td>-0.125722</td>\n",
       "      <td>-0.168288</td>\n",
       "      <td>-0.079221</td>\n",
       "      <td>0.092995</td>\n",
       "      <td>0.116980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_male</th>\n",
       "      <td>0.131900</td>\n",
       "      <td>-0.114631</td>\n",
       "      <td>-0.245489</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.075254</td>\n",
       "      <td>-0.041846</td>\n",
       "      <td>0.045831</td>\n",
       "      <td>0.026242</td>\n",
       "      <td>-0.082853</td>\n",
       "      <td>-0.074115</td>\n",
       "      <td>0.125722</td>\n",
       "      <td>0.168288</td>\n",
       "      <td>0.079221</td>\n",
       "      <td>-0.092995</td>\n",
       "      <td>-0.116980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_Children</th>\n",
       "      <td>0.120693</td>\n",
       "      <td>0.358489</td>\n",
       "      <td>0.352285</td>\n",
       "      <td>0.075254</td>\n",
       "      <td>-0.075254</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.103165</td>\n",
       "      <td>-0.358291</td>\n",
       "      <td>-0.122055</td>\n",
       "      <td>-0.041614</td>\n",
       "      <td>-0.021318</td>\n",
       "      <td>0.051117</td>\n",
       "      <td>-0.150918</td>\n",
       "      <td>-0.072962</td>\n",
       "      <td>0.141971</td>\n",
       "      <td>0.111105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_Teenage</th>\n",
       "      <td>0.100625</td>\n",
       "      <td>-0.003250</td>\n",
       "      <td>-0.014161</td>\n",
       "      <td>0.041846</td>\n",
       "      <td>-0.041846</td>\n",
       "      <td>-0.103165</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.494233</td>\n",
       "      <td>-0.168365</td>\n",
       "      <td>0.000614</td>\n",
       "      <td>-0.043453</td>\n",
       "      <td>0.028631</td>\n",
       "      <td>0.048907</td>\n",
       "      <td>0.078912</td>\n",
       "      <td>-0.074106</td>\n",
       "      <td>-0.041328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_Adult</th>\n",
       "      <td>0.100910</td>\n",
       "      <td>-0.131867</td>\n",
       "      <td>-0.187289</td>\n",
       "      <td>-0.045831</td>\n",
       "      <td>0.045831</td>\n",
       "      <td>-0.358291</td>\n",
       "      <td>-0.494233</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.584728</td>\n",
       "      <td>-0.006871</td>\n",
       "      <td>0.118809</td>\n",
       "      <td>-0.067204</td>\n",
       "      <td>0.157165</td>\n",
       "      <td>0.015632</td>\n",
       "      <td>-0.062301</td>\n",
       "      <td>-0.127994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_Elder</th>\n",
       "      <td>-0.301990</td>\n",
       "      <td>-0.080458</td>\n",
       "      <td>-0.009268</td>\n",
       "      <td>-0.026242</td>\n",
       "      <td>0.026242</td>\n",
       "      <td>-0.122055</td>\n",
       "      <td>-0.168365</td>\n",
       "      <td>-0.584728</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.023855</td>\n",
       "      <td>-0.094339</td>\n",
       "      <td>0.033874</td>\n",
       "      <td>-0.132237</td>\n",
       "      <td>-0.035444</td>\n",
       "      <td>0.027340</td>\n",
       "      <td>0.137345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked_C</th>\n",
       "      <td>-0.243292</td>\n",
       "      <td>-0.059528</td>\n",
       "      <td>-0.011069</td>\n",
       "      <td>0.082853</td>\n",
       "      <td>-0.082853</td>\n",
       "      <td>-0.041614</td>\n",
       "      <td>0.000614</td>\n",
       "      <td>-0.006871</td>\n",
       "      <td>0.023855</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.148258</td>\n",
       "      <td>-0.778359</td>\n",
       "      <td>-0.035399</td>\n",
       "      <td>-0.199984</td>\n",
       "      <td>0.038225</td>\n",
       "      <td>0.151024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked_Q</th>\n",
       "      <td>0.221009</td>\n",
       "      <td>-0.026354</td>\n",
       "      <td>-0.081228</td>\n",
       "      <td>0.074115</td>\n",
       "      <td>-0.074115</td>\n",
       "      <td>-0.021318</td>\n",
       "      <td>-0.043453</td>\n",
       "      <td>0.118809</td>\n",
       "      <td>-0.094339</td>\n",
       "      <td>-0.148258</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.496624</td>\n",
       "      <td>0.311829</td>\n",
       "      <td>-0.118679</td>\n",
       "      <td>-0.016363</td>\n",
       "      <td>-0.137169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked_S</th>\n",
       "      <td>0.081720</td>\n",
       "      <td>0.070941</td>\n",
       "      <td>0.063036</td>\n",
       "      <td>-0.125722</td>\n",
       "      <td>0.125722</td>\n",
       "      <td>0.051117</td>\n",
       "      <td>0.028631</td>\n",
       "      <td>-0.067204</td>\n",
       "      <td>0.033874</td>\n",
       "      <td>-0.778359</td>\n",
       "      <td>-0.496624</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.162041</td>\n",
       "      <td>0.252097</td>\n",
       "      <td>-0.020182</td>\n",
       "      <td>-0.055697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare_Low_fare</th>\n",
       "      <td>0.450238</td>\n",
       "      <td>-0.232991</td>\n",
       "      <td>-0.244930</td>\n",
       "      <td>-0.168288</td>\n",
       "      <td>0.168288</td>\n",
       "      <td>-0.150918</td>\n",
       "      <td>0.048907</td>\n",
       "      <td>0.157165</td>\n",
       "      <td>-0.132237</td>\n",
       "      <td>-0.035399</td>\n",
       "      <td>0.311829</td>\n",
       "      <td>-0.162041</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.313128</td>\n",
       "      <td>-0.324571</td>\n",
       "      <td>-0.281527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare_median_fare</th>\n",
       "      <td>0.200349</td>\n",
       "      <td>-0.202833</td>\n",
       "      <td>-0.203853</td>\n",
       "      <td>-0.079221</td>\n",
       "      <td>0.079221</td>\n",
       "      <td>-0.072962</td>\n",
       "      <td>0.078912</td>\n",
       "      <td>0.015632</td>\n",
       "      <td>-0.035444</td>\n",
       "      <td>-0.199984</td>\n",
       "      <td>-0.118679</td>\n",
       "      <td>0.252097</td>\n",
       "      <td>-0.313128</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.333725</td>\n",
       "      <td>-0.289467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare_Average_fare</th>\n",
       "      <td>-0.042044</td>\n",
       "      <td>0.121717</td>\n",
       "      <td>0.161366</td>\n",
       "      <td>0.092995</td>\n",
       "      <td>-0.092995</td>\n",
       "      <td>0.141971</td>\n",
       "      <td>-0.074106</td>\n",
       "      <td>-0.062301</td>\n",
       "      <td>0.027340</td>\n",
       "      <td>0.038225</td>\n",
       "      <td>-0.016363</td>\n",
       "      <td>-0.020182</td>\n",
       "      <td>-0.324571</td>\n",
       "      <td>-0.333725</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.300046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare_high_fare</th>\n",
       "      <td>-0.453875</td>\n",
       "      <td>0.336510</td>\n",
       "      <td>0.253944</td>\n",
       "      <td>0.116980</td>\n",
       "      <td>-0.116980</td>\n",
       "      <td>0.111105</td>\n",
       "      <td>-0.041328</td>\n",
       "      <td>-0.127994</td>\n",
       "      <td>0.137345</td>\n",
       "      <td>0.151024</td>\n",
       "      <td>-0.137169</td>\n",
       "      <td>-0.055697</td>\n",
       "      <td>-0.281527</td>\n",
       "      <td>-0.289467</td>\n",
       "      <td>-0.300046</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Pclass     SibSp     Parch  Sex_female  Sex_male  \\\n",
       "Pclass             1.000000  0.083081  0.018443   -0.131900  0.131900   \n",
       "SibSp              0.083081  1.000000  0.414838    0.114631 -0.114631   \n",
       "Parch              0.018443  0.414838  1.000000    0.245489 -0.245489   \n",
       "Sex_female        -0.131900  0.114631  0.245489    1.000000 -1.000000   \n",
       "Sex_male           0.131900 -0.114631 -0.245489   -1.000000  1.000000   \n",
       "Age_Children       0.120693  0.358489  0.352285    0.075254 -0.075254   \n",
       "Age_Teenage        0.100625 -0.003250 -0.014161    0.041846 -0.041846   \n",
       "Age_Adult          0.100910 -0.131867 -0.187289   -0.045831  0.045831   \n",
       "Age_Elder         -0.301990 -0.080458 -0.009268   -0.026242  0.026242   \n",
       "Embarked_C        -0.243292 -0.059528 -0.011069    0.082853 -0.082853   \n",
       "Embarked_Q         0.221009 -0.026354 -0.081228    0.074115 -0.074115   \n",
       "Embarked_S         0.081720  0.070941  0.063036   -0.125722  0.125722   \n",
       "Fare_Low_fare      0.450238 -0.232991 -0.244930   -0.168288  0.168288   \n",
       "Fare_median_fare   0.200349 -0.202833 -0.203853   -0.079221  0.079221   \n",
       "Fare_Average_fare -0.042044  0.121717  0.161366    0.092995 -0.092995   \n",
       "Fare_high_fare    -0.453875  0.336510  0.253944    0.116980 -0.116980   \n",
       "\n",
       "                   Age_Children  Age_Teenage  Age_Adult  Age_Elder  \\\n",
       "Pclass                 0.120693     0.100625   0.100910  -0.301990   \n",
       "SibSp                  0.358489    -0.003250  -0.131867  -0.080458   \n",
       "Parch                  0.352285    -0.014161  -0.187289  -0.009268   \n",
       "Sex_female             0.075254     0.041846  -0.045831  -0.026242   \n",
       "Sex_male              -0.075254    -0.041846   0.045831   0.026242   \n",
       "Age_Children           1.000000    -0.103165  -0.358291  -0.122055   \n",
       "Age_Teenage           -0.103165     1.000000  -0.494233  -0.168365   \n",
       "Age_Adult             -0.358291    -0.494233   1.000000  -0.584728   \n",
       "Age_Elder             -0.122055    -0.168365  -0.584728   1.000000   \n",
       "Embarked_C            -0.041614     0.000614  -0.006871   0.023855   \n",
       "Embarked_Q            -0.021318    -0.043453   0.118809  -0.094339   \n",
       "Embarked_S             0.051117     0.028631  -0.067204   0.033874   \n",
       "Fare_Low_fare         -0.150918     0.048907   0.157165  -0.132237   \n",
       "Fare_median_fare      -0.072962     0.078912   0.015632  -0.035444   \n",
       "Fare_Average_fare      0.141971    -0.074106  -0.062301   0.027340   \n",
       "Fare_high_fare         0.111105    -0.041328  -0.127994   0.137345   \n",
       "\n",
       "                   Embarked_C  Embarked_Q  Embarked_S  Fare_Low_fare  \\\n",
       "Pclass              -0.243292    0.221009    0.081720       0.450238   \n",
       "SibSp               -0.059528   -0.026354    0.070941      -0.232991   \n",
       "Parch               -0.011069   -0.081228    0.063036      -0.244930   \n",
       "Sex_female           0.082853    0.074115   -0.125722      -0.168288   \n",
       "Sex_male            -0.082853   -0.074115    0.125722       0.168288   \n",
       "Age_Children        -0.041614   -0.021318    0.051117      -0.150918   \n",
       "Age_Teenage          0.000614   -0.043453    0.028631       0.048907   \n",
       "Age_Adult           -0.006871    0.118809   -0.067204       0.157165   \n",
       "Age_Elder            0.023855   -0.094339    0.033874      -0.132237   \n",
       "Embarked_C           1.000000   -0.148258   -0.778359      -0.035399   \n",
       "Embarked_Q          -0.148258    1.000000   -0.496624       0.311829   \n",
       "Embarked_S          -0.778359   -0.496624    1.000000      -0.162041   \n",
       "Fare_Low_fare       -0.035399    0.311829   -0.162041       1.000000   \n",
       "Fare_median_fare    -0.199984   -0.118679    0.252097      -0.313128   \n",
       "Fare_Average_fare    0.038225   -0.016363   -0.020182      -0.324571   \n",
       "Fare_high_fare       0.151024   -0.137169   -0.055697      -0.281527   \n",
       "\n",
       "                   Fare_median_fare  Fare_Average_fare  Fare_high_fare  \n",
       "Pclass                     0.200349          -0.042044       -0.453875  \n",
       "SibSp                     -0.202833           0.121717        0.336510  \n",
       "Parch                     -0.203853           0.161366        0.253944  \n",
       "Sex_female                -0.079221           0.092995        0.116980  \n",
       "Sex_male                   0.079221          -0.092995       -0.116980  \n",
       "Age_Children              -0.072962           0.141971        0.111105  \n",
       "Age_Teenage                0.078912          -0.074106       -0.041328  \n",
       "Age_Adult                  0.015632          -0.062301       -0.127994  \n",
       "Age_Elder                 -0.035444           0.027340        0.137345  \n",
       "Embarked_C                -0.199984           0.038225        0.151024  \n",
       "Embarked_Q                -0.118679          -0.016363       -0.137169  \n",
       "Embarked_S                 0.252097          -0.020182       -0.055697  \n",
       "Fare_Low_fare             -0.313128          -0.324571       -0.281527  \n",
       "Fare_median_fare           1.000000          -0.333725       -0.289467  \n",
       "Fare_Average_fare         -0.333725           1.000000       -0.300046  \n",
       "Fare_high_fare            -0.289467          -0.300046        1.000000  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.corr() # Pandas dataframe.corr()用于查找数据帧中所有列的成对相关性。任何na值会自动排除。对于 DataFrame 中的任何非数字数据类型列，将忽略该列。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "88f1e6fb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T08:10:48.331681Z",
     "start_time": "2022-04-27T08:10:44.723215Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(X.corr(),annot=True,cmap='RdYlGn',linewidths=0.2)\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(20,12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ea2f6fe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "71df1e1d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T07:59:39.945742Z",
     "start_time": "2022-04-27T07:59:39.940838Z"
    }
   },
   "outputs": [],
   "source": [
    "# 在热图结果中：\n",
    "# 正相关：如果 A 的增加导致特征 B 的增加，那么这两个特征是正相关的。 完全正相关特征的值为 1。\n",
    "# 负相关：如果 A 的增加导致特征 B 的减少，那么这两个特征是负相关的。 那么完全负相关特征的值为-1。\n",
    "# 在我们的训练数据中有两个高度或完全相关的特征会导致多重共线性，因此最好将它们删除。\n",
    "\n",
    "# 在上述热图中，我们可以看到没有高度相关的特征。 在特征 Parch 和 SibSp 之间，相关性的最高值为 0.41。 因此无需删除任何功能。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "4abf5bb8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T08:11:48.379479Z",
     "start_time": "2022-04-27T08:11:48.349643Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 如果上述方式还不够明显，我们也可以在代码的帮助下通过定义像 0.6 这样的阈值来做到这一点\n",
    "threshold=0.6\n",
    "# find and remove correlated features\n",
    "def correlation(dataset, threshold):\n",
    "    col_corr = set()  # Set of all the names of correlated columns\n",
    "    corr_matrix = dataset.corr()\n",
    "    for i in range(len(corr_matrix.columns)):\n",
    "        for j in range(i):\n",
    "            if corr_matrix.iloc[i, j] > threshold: # we are interested in absolute coeff value\n",
    "                colname = corr_matrix.columns[i]  # getting the name of column\n",
    "                col_corr.add(colname)\n",
    "    return col_corr\n",
    "\n",
    "correlation(X,threshold)\n",
    "# 如果输出的结果为set(),意味着没有高相关的特征，不需要去删除特征"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07adc676-fc62-470e-989e-7427442233be",
   "metadata": {},
   "source": [
    "sklean提供了一些特征选择的方法，比如说VarianceThreshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "5703a0f6-8b7e-4062-8d2b-c5f734b1269d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 1, 0, ..., 0, 0, 0],\n",
       "       [1, 1, 0, ..., 0, 0, 1],\n",
       "       [3, 0, 0, ..., 1, 0, 0],\n",
       "       ...,\n",
       "       [3, 1, 2, ..., 0, 1, 0],\n",
       "       [1, 0, 0, ..., 0, 1, 0],\n",
       "       [3, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dataframe转np.array\n",
    "X = X.values\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "7fb74723-9efc-4c79-be6c-2c2364952394",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3 1 0 ... 0 0 0]\n",
      " [1 1 0 ... 0 0 1]\n",
      " [3 0 0 ... 1 0 0]\n",
      " ...\n",
      " [3 1 2 ... 0 1 0]\n",
      " [1 0 0 ... 0 1 0]\n",
      " [3 0 0 ... 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold\n",
    "import numpy as np\n",
    "\n",
    "# 创建示例数据集\n",
    "# X = np.array([[0, 2, 0, 3],\n",
    "#               [0, 1, 4, 3],\n",
    "#               [0, 1, 1, 3],\n",
    "#               [0, 1, 0, 3]])\n",
    "\n",
    "# columns = X.columns\n",
    "\n",
    "# 创建 VarianceThreshold 对象，设置方差阈值为0.1\n",
    "selector = VarianceThreshold(threshold=0.1)\n",
    "\n",
    "# dataframe转np.array\n",
    "\n",
    "# X = X.values\n",
    "\n",
    "# 进行特征选择\n",
    "X_selected = selector.fit_transform(X)\n",
    "\n",
    "# 将选择后的特征矩阵转换为 DataFrame\n",
    "# X_selected_df = pd.DataFrame(X_selected, columns=columns[selector.get_support()])\n",
    "\n",
    "# 设置 Pandas 显示选项，确保所有列都会显示\n",
    "# pd.set_option('display.max_columns', None)\n",
    "\n",
    "X = X_selected\n",
    "# 查看选择后的特征矩阵\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "a3defc74-8d65-44a9-93af-f34658063862",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_selected_df.to_csv('tmp.csv', index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1beb9f0",
   "metadata": {},
   "source": [
    "## Model Training - 简版"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "9c540127",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T08:31:13.646934Z",
     "start_time": "2022-04-27T08:31:13.630496Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\ntest_size:test样本的占比\\nrandom_state: 可以为整数、RandomState实例或None，默认为None\\n①若为None时，每次生成的数据都是随机，可能不一样\\n②若为整数时，每次生成的数据都相同\\n'"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "\"\"\"\n",
    "test_size:test样本的占比\n",
    "random_state: 可以为整数、RandomState实例或None，默认为None\n",
    "①若为None时，每次生成的数据都是随机，可能不一样\n",
    "②若为整数时，每次生成的数据都相同\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "160db66e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T08:31:14.131650Z",
     "start_time": "2022-04-27T08:31:14.121353Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix, classification_report\n",
    "from sklearn.model_selection import KFold, cross_val_score, cross_val_predict ## Cross Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6fafddb",
   "metadata": {},
   "source": [
    "### 朴素贝叶斯Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "cb437505",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T08:33:19.078501Z",
     "start_time": "2022-04-27T08:33:18.227396Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------GaussianNB Naive Bayes -------\n",
      "The accuracy Gaussian Naive Bayes Classifier is 76.87\n",
      "[[121  36]\n",
      " [ 26  85]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#################################### Naive Bayes ########################\n",
    "# 调用朴素贝叶斯算法进行预测\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "model= GaussianNB()\n",
    "model.fit(X_train,y_train)\n",
    "prediction_gnb=model.predict(X_test)\n",
    "\n",
    "print('--------GaussianNB Naive Bayes -------')\n",
    "print('The accuracy Gaussian Naive Bayes Classifier is',round(accuracy_score(prediction_gnb,y_test)*100,2)) # metrics.accuracy_score进行准确率衡量\n",
    "\n",
    "# y_pred = cross_val_predict(model,X,y,cv=10)  \n",
    "\n",
    "# prediction_gnb=model.predict(X_test)\n",
    "sns.heatmap(confusion_matrix(y_test,prediction_gnb),annot=True,fmt='3.0f',cmap=\"Accent_r\") # heatmap显示混淆矩阵\n",
    "print(confusion_matrix(y_test,prediction_gnb))\n",
    "\n",
    "plt.title('Confusion Matrix', y=1, size=15);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "7121d538-b38f-40a5-b162-df0ea276df2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross validated score for Gaussian Naive Bayes classifier is: 77.0\n",
      "[[430 119]\n",
      " [ 86 256]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# K折交叉验证,得到更准缺的结果\n",
    "kfold = KFold(n_splits=10,shuffle=True,random_state=42) # split the data into 10 equal parts，这不是只是帮助理解K折交叉验证的原理，可删除\n",
    "# for train, test in kfold.split(X):\n",
    "#     print(train,test)\n",
    "#     print(\"--------\")\n",
    "#     print(np.array(X)[train], np.array(X)[test])\n",
    "#     print(\"*\"*50)\n",
    "\n",
    "result_gnb=cross_val_score(model,X,y,cv=10,scoring='accuracy')  # cv=10 表示10折\n",
    "\n",
    "print('The cross validated score for Gaussian Naive Bayes classifier is:',round(result_gnb.mean()*100,2))\n",
    "\n",
    "y_pred = cross_val_predict(model,X,y,cv=10)  \n",
    "\n",
    "sns.heatmap(confusion_matrix(y,y_pred),annot=True,fmt='3.0f',cmap=\"Accent_r\") # heatmap显示混淆矩阵\n",
    "print(confusion_matrix(y,y_pred))\n",
    "\n",
    "plt.title('Confusion Matrix', y=1, size=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a47418c",
   "metadata": {},
   "source": [
    "### 支持向量机SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "94e5c441",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T08:38:03.032720Z",
     "start_time": "2022-04-27T08:38:00.810242Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------SVM -------\n",
      "The accuracy SVM is 81.34\n",
      "The cross validated score for SVM is: 80.81\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#################################### SVM ########################\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "model = SVC()\n",
    "model.fit(X_train,y_train)\n",
    "prediction_svm=model.predict(X_test)\n",
    "\n",
    "print('--------SVM -------')\n",
    "print('The accuracy SVM is',round(accuracy_score(prediction_svm,y_test)*100,2))\n",
    "\n",
    "kfold = KFold(n_splits=10,shuffle=True,random_state=42) # split the data into 10 equal parts\n",
    "\n",
    "result_svm=cross_val_score(model,X,y,cv=10,scoring='accuracy') \n",
    "\n",
    "print('The cross validated score for SVM is:',round(result_svm.mean()*100,2))\n",
    "\n",
    "y_pred = cross_val_predict(model,X,y,cv=10)\n",
    "sns.heatmap(confusion_matrix(y,y_pred),annot=True,fmt='3.0f',cmap=\"Accent_r\")\n",
    "plt.title('Confusion Matrix', y=1, size=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc197afe",
   "metadata": {},
   "source": [
    "### 神经网络KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "50163f9e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T08:39:13.593858Z",
     "start_time": "2022-04-27T08:39:11.823313Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------KNN -------\n",
      "The accuracy KNN Classifier is 80.22\n",
      "The cross validated score for KNN classifier is: 79.91\n"
     ]
    },
    {
     "data": {
      "image/png": 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lmv+2BAAAVCnCAQAAMBAOAACAgXAAAAAMhAMAAGAgHAAAAAPhAAAAGGrMr2yeun1qdZcA1DhHXz5a3SUANdKiRYsqdf8V+TNp6q0Vt6+qwsoBAAAwEA4AAICBcAAAAAyEAwAAYCAcAAAAA+EAAAAYCAcAAMBAOAAAAAbCAQAAMBAOAACAgXAAAAAMhAMAAGAgHAAAAAPhAAAAGAgHAADAQDgAAKAGev7552Wz2fT4449723r27CmbzWZs48aNM+YdOnRI8fHxCgwMVEREhCZNmqSioqJyHduvIk4AAABUnB07dmjRokVq27btJX0PP/ywpk2b5n0dGBjo/bq4uFjx8fFyOp3aunWrjh49qlGjRqlWrVp67rnnynx8Vg4AAKhBzpw5o+HDh2vx4sWqW7fuJf2BgYFyOp3ezeFwePvWrVunPXv2aNmyZWrfvr369++v6dOna968eSosLCxzDYQDAAAqkcfjUX5+vrF5PJ5SxyckJCg+Pl59+vQpsX/58uUKDw9X69atlZSUpLNnz3r70tPT1aZNG0VGRnrb+vbtq/z8fO3evbvMNRMOAACoRMnJyQoJCTG25OTkEse+8cYb+vTTT0vtf/DBB7Vs2TJ99NFHSkpK0muvvaYRI0Z4+91utxEMJHlfu93uMtfMPQcAAFSipKQkJSYmGm12u/2ScYcPH9Zjjz2mlJQUBQQElLivsWPHer9u06aNoqKi1Lt3b2VnZ6tZs2YVVjMrBwAAVCK73S6Hw2FsJYWDjIwM5ebmqmPHjvLz85Ofn5/S0tI0Z84c+fn5qbi4+JI5Xbp0kSTt379fkuR0OpWTk2OMufja6XSWuWbCAQAANUDv3r2VlZWlzMxM79apUycNHz5cmZmZ8vX1vWROZmamJCkqKkqS5HK5lJWVpdzcXO+YlJQUORwOxcTElLkWLisAAFADBAcHq3Xr1kZbUFCQ6tWrp9atWys7O1srVqzQgAEDVK9ePe3atUsTJ05U9+7dvR95jIuLU0xMjEaOHKkZM2bI7XZrypQpSkhIKHG1ojSEAwAAfgH8/f21fv16zZo1SwUFBWrUqJGGDBmiKVOmeMf4+vpq9erVeuSRR+RyuRQUFKTRo0cbz0UoC8IBAAA11MaNG71fN2rUSGlpaVec06RJE61Zs+aajss9BwAAwEA4AAAABsIBAAAwEA4AAICBcAAAAAyEAwAAYCAcAAAAA+EAAAAYCAcAAMBAOAAAAAbCAQAAMBAOAACAgXAAAAAMhAMAAGAgHAAAAAPhAAAAGAgHAADAQDgAAAAGwgEAADD4VXcBAADUND2+G1rdJVQrVg4AAICBcAAAAAyEAwAAYCAcAAAAA+EAAAAYCAcAAMBAOAAAAAbCAQAAMBAOAACAgXAAAAAMhAMAAGAgHAAAAAPhAAAAGAgHAADAQDgAAAAGwgEAADAQDgAAqIGef/552Ww2Pf744962c+fOKSEhQfXq1VOdOnU0ZMgQ5eTkGPMOHTqk+Ph4BQYGKiIiQpMmTVJRUVG5jk04AACghtmxY4cWLVqktm3bGu0TJ07UqlWr9NZbbyktLU1HjhzRvffe6+0vLi5WfHy8CgsLtXXrVi1dulRLlizR008/Xa7jEw4AAKhBzpw5o+HDh2vx4sWqW7eutz0vL08vv/yyXnzxRd15552KjY3VK6+8oq1bt+qTTz6RJK1bt0579uzRsmXL1L59e/Xv31/Tp0/XvHnzVFhYWOYaCAcAAFQij8ej/Px8Y/N4PKWOT0hIUHx8vPr06WO0Z2Rk6Pz580Z7dHS0GjdurPT0dElSenq62rRpo8jISO+Yvn37Kj8/X7t37y5zzYQDAAAqUXJyskJCQowtOTm5xLFvvPGGPv300xL73W63/P39FRoaarRHRkbK7XZ7x/w0GFzsv9hXVn5lHgkAAMotKSlJiYmJRpvdbr9k3OHDh/XYY48pJSVFAQEBVVVeiVg5AACgEtntdjkcDmMrKRxkZGQoNzdXHTt2lJ+fn/z8/JSWlqY5c+bIz89PkZGRKiws1KlTp4x5OTk5cjqdkiSn03nJpxcuvr44piwIBwAA1AC9e/dWVlaWMjMzvVunTp00fPhw79e1atVSamqqd86+fft06NAhuVwuSZLL5VJWVpZyc3O9Y1JSUuRwOBQTE1PmWrisAABADRAcHKzWrVsbbUFBQapXr563fcyYMUpMTFRYWJgcDocmTJggl8ul2267TZIUFxenmJgYjRw5UjNmzJDb7daUKVOUkJBQ4mpFaQgHAAD8QsycOVM+Pj4aMmSIPB6P+vbtq/nz53v7fX19tXr1aj3yyCNyuVwKCgrS6NGjNW3atHIdx2ZZllXRxV+NqdunVncJQI1z9OWj1V0CUCMtWrSoUvf/0cqyf+zvSnr95pYK21dV4Z4DAABgIBwAAAAD4QAAABgIBwAAwEA4AAAABsIBAAAwEA4AAICBcAAAAAyEAwAAYCAcAAAAA+EAAAAYCAcAAMBAOAAAAAZ+ZfN1Zs+qPfrsn5+pRd8Wih0RqzPHzmhV4qoSx3Yd31WNuzTWyW9Oas/qPTr+5XF5TnsUVD9Ize9srpZ9W1Zx9UDlu/vuuzVw4MBL2j0ejx599FFJUmJiolq2vPTvf1ZWll566aVKrxGobISD68j3X3+v/Rv2K7RRqLctsF6gBs8dbIzL/ihbe9fsVVS7KEnSiYMnFOAIkGucS4H1AnXsq2Pa8Y8dsvnY1OKuFlV4BkDlS0lJ0aZNm4y2iRMn6uDBg97XCxculJ/f//33GRQUpD/96U/KyMioqjKBSkU4uE6cP3de6QvSdeuYW7X73f/7PeU+Pj6qHVrbGHs447Aa39pYtQJqSZKa9Whm9NeJqKPvv/peh3ccJhzgV8fj8cjj8XhfN2zYUA0aNNDy5cu9bWfPnjXmdO7cWYWFhYQD/GqUOxwcP35c//jHP5Seni632y1Jcjqduv322/XQQw+pfv36FV4krt3OpTvVoF0DOVs7jXDwcycOnNCpb06p06hOl91f4Q+FstexV3SZQI3TrVs3ud1u7d+/v9QxXbt21c6dO1VYWFiFlQGVp1w3JO7YsUMtWrTQnDlzFBISou7du6t79+4KCQnRnDlzFB0drZ07d15xPx6PR/n5+cZWVFh01SeBy/sm/RudPHhS7e5vd8Wx2WnZcjRwqH6L0kPesS+P6dC2Q2rWq1mpY4BfAz8/P916663asmVLqWNuvPFG3XDDDfr444+rsDKgcpVr5WDChAkaOnSoFi5cKJvNZvRZlqVx48ZpwoQJSk9Pv+x+kpOT9eyzzxptPf6jh3o+3LM85aAMCr4vUMayDPWa3Eu+/r6XHVtUWKRv0r/RLYNuKXXMqcOntHnWZrUe3FpRbaIqulygRunQoYMCAgIu+39a165d9e233xr3JOCXr0fYexW4t9L/T62pyhUOPvvsMy1ZsuSSYCBJNptNEydOVIcOHa64n6SkJCUmJhptM3bNKE8pKKOTB07Kk+/R2j+t9bZZFyzl7svVVylf6f5X7pePz48LSIe3H1axp1hNuzUtcV953+Vpw/Mb1KxXM7Ue3LpK6geqU7du3bRr1y6dPn26xH5/f3917txZ771XkT9IgOpXrnDgdDq1fft2RUdHl9i/fft2RUZGXnE/drtddrt5vdrPn3sjK0PkLZHq/1x/o23b4m1yNHCoVXwrbzCQpK/TvtYNHW9QgCPgkv3kfZun1ORUNe3WVO2GXvnyBPBLV69ePbVo0ULz588vdUxsbKz8/Py0bdu2KqwMqHzl+on85JNPauzYscrIyFDv3r29QSAnJ0epqalavHixXnjhhUopFFenVu1axkcXJcnP7if/Ov5G++mc08rdl6seT/a4ZB+nDp/ShuQNimobpej+0frh1A+SJJuPrcQgAfwadO3aVXl5efr8888vOyYzM1MFBQVVWBlQ+coVDhISEhQeHq6ZM2dq/vz5Ki4uliT5+voqNjZWS5Ys0f33318phaJyfZ32tQLDAhXV+tL7CA7vOCzPaY8Objmog1sOetuDwoN0z8x7qrBKoGrYbDa5XC6lp6fLsqwSx0RGRurmm2/WrFmzqrY4oArYrNL+5l/B+fPndfz4cUlSeHi4atWqdU2FTN0+9ZrmA79GR18+Wt0lADXSokWLKnX/F9KSK2xfPj2SKmxfVeWqL/TXqlVLUVHcrQ4AwK8Nv3gJAAAYCAcAAMBAOAAAAAbCAQAAMBAOAACAgXAAAAAMhAMAAGAgHAAAAAPhAAAAGAgHAADAQDgAAAAGwgEAADXEggUL1LZtWzkcDjkcDrlcLn3wwQfe/p49e8pmsxnbuHHjjH0cOnRI8fHxCgwMVEREhCZNmqSioqJy1XHVv3gJAABUrIYNG+r555/XzTffLMuytHTpUg0aNEj//ve/dcstt0iSHn74YU2bNs07JzAw0Pt1cXGx4uPj5XQ6tXXrVh09elSjRo1SrVq19Nxzz5W5DsIBAAA1xMCBA43Xf/nLX7RgwQJ98skn3nAQGBgop9NZ4vx169Zpz549Wr9+vSIjI9W+fXtNnz5dkydP1tSpU+Xv71+mOrisAABAJfJ4PMrPzzc2j8dzxXnFxcV64403VFBQIJfL5W1fvny5wsPD1bp1ayUlJens2bPevvT0dLVp00aRkZHetr59+yo/P1+7d+8uc82EAwAAKlFycrJCQkKMLTk5udTxWVlZqlOnjux2u8aNG6eVK1cqJiZGkvTggw9q2bJl+uijj5SUlKTXXntNI0aM8M51u91GMJDkfe12u8tcM5cVAACoRElJSUpMTDTa7HZ7qeNbtmypzMxM5eXl6e2339bo0aOVlpammJgYjR071juuTZs2ioqKUu/evZWdna1mzZpVWM2EAwAAKpHdbr9sGPg5f39/NW/eXJIUGxurHTt2aPbs2Vq0aNElY7t06SJJ2r9/v5o1ayan06nt27cbY3JyciSp1PsUSsJlBQAAarALFy6Ueo9CZmamJCkqKkqS5HK5lJWVpdzcXO+YlJQUORwO76WJsmDlAACAGiIpKUn9+/dX48aNdfr0aa1YsUIbN27U2rVrlZ2drRUrVmjAgAGqV6+edu3apYkTJ6p79+5q27atJCkuLk4xMTEaOXKkZsyYIbfbrSlTpighIaFcqxeEAwAAaojc3FyNGjVKR48eVUhIiNq2bau1a9fqrrvu0uHDh7V+/XrNmjVLBQUFatSokYYMGaIpU6Z45/v6+mr16tV65JFH5HK5FBQUpNGjRxvPRSgLwgEAADXEyy+/XGpfo0aNlJaWdsV9NGnSRGvWrLmmOrjnAAAAGAgHAADAQDgAAAAGwgEAADAQDgAAgIFwAAAADIQDAABgIBwAAAAD4QAAABgIBwAAwEA4AAAABsIBAAAwEA4AAICBcAAAAAyEAwAAYCAcAAAAA+EAAAAY/Kq7AAAAahqfHvWqu4RqxcoBAAAwEA4AAICBcAAAAAyEAwAAYCAcAAAAA+EAAAAYCAcAAMBAOAAAAAbCAQAAMBAOAACAgXAAAAAMhAMAAGAgHAAAAAPhAAAAGAgHAADAQDgAAAAGwgEAADAQDgAAgIFwAABADbFgwQK1bdtWDodDDodDLpdLH3zwgbf/3LlzSkhIUL169VSnTh0NGTJEOTk5xj4OHTqk+Ph4BQYGKiIiQpMmTVJRUVG56iAcAABQQzRs2FDPP/+8MjIytHPnTt15550aNGiQdu/eLUmaOHGiVq1apbfeektpaWk6cuSI7r33Xu/84uJixcfHq7CwUFu3btXSpUu1ZMkSPf300+Wqw2ZZllWhZ3aVpm6fWt0lADXO0ZePVncJQI20aNGiSj7C3ytwX2OvaXZYWJj+9re/6b777lP9+vW1YsUK3XfffZKkL774Qq1atVJ6erpuu+02ffDBB7r77rt15MgRRUZGSpIWLlyoyZMn69ixY/L39y/TMVk5AACgEnk8HuXn5xubx+O54rzi4mK98cYbKigokMvlUkZGhs6fP68+ffp4x0RHR6tx48ZKT0+XJKWnp6tNmzbeYCBJffv2VX5+vnf1oSwIBwAAVKLk5GSFhIQYW3Jycqnjs7KyVKdOHdntdo0bN04rV65UTEyM3G63/P39FRoaaoyPjIyU2+2WJLndbiMYXOy/2FdWfmUeCQAAyi0pKUmJiYlGm91uL3V8y5YtlZmZqby8PL399tsaPXq00tLSKrtMA+EAAIBKZLfbLxsGfs7f31/NmzeXJMXGxmrHjh2aPXu2fvvb36qwsFCnTp0yVg9ycnLkdDolSU6nU9u3bzf2d/HTDBfHlAWXFQAAqMEuXLggj8ej2NhY1apVS6mpqd6+ffv26dChQ3K5XJIkl8ulrKws5ebmesekpKTI4XAoJiamzMdk5QAAgBoiKSlJ/fv3V+PGjXX69GmtWLFCGzdu1Nq1axUSEqIxY8YoMTFRYWFhcjgcmjBhglwul2677TZJUlxcnGJiYjRy5EjNmDFDbrdbU6ZMUUJCQrlWLwgHAADUELm5uRo1apSOHj2qkJAQtW3bVmvXrtVdd90lSZo5c6Z8fHw0ZMgQeTwe9e3bV/Pnz/fO9/X11erVq/XII4/I5XIpKChIo0eP1rRp08pVB885AGownnMAlOx6es5BdeCeAwAAYCAcAAAAA+EAAAAYCAcAAMBAOAAAAAbCAQAAMBAOAACAgXAAAAAMhAMAAGAgHAAAAAPhAAAAGAgHAADAQDgAAAAGwgEAADAQDgAAgIFwAAAADH7VXcBFR18+Wt0lADXOLv8B1V0CcF1yTYissH2lz62wXVUZVg4AAICBcAAAAAyEAwAAYCAcAAAAA+EAAAAYCAcAAMBAOAAAAAbCAQAAMBAOAACAgXAAAAAMhAMAAGAgHAAAAAPhAAAAGAgHAADAQDgAAAAGwgEAADAQDgAAgIFwAAAADIQDAABgIBwAAFBDJCcnq3PnzgoODlZERIQGDx6sffv2GWN69uwpm81mbOPGjTPGHDp0SPHx8QoMDFRERIQmTZqkoqKiMtfhVyFnAwAArllaWpoSEhLUuXNnFRUV6b/+678UFxenPXv2KCgoyDvu4Ycf1rRp07yvAwMDvV8XFxcrPj5eTqdTW7du1dGjRzVq1CjVqlVLzz33XJnqIBwAAFCJPB6PPB6P0Wa322W32y8Z++GHHxqvlyxZooiICGVkZKh79+7e9sDAQDmdzhKPt27dOu3Zs0fr169XZGSk2rdvr+nTp2vy5MmaOnWq/P39r1gzlxUAAKhEycnJCgkJMbbk5OQyzc3Ly5MkhYWFGe3Lly9XeHi4WrduraSkJJ09e9bbl56erjZt2igyMtLb1rdvX+Xn52v37t1lOi4rBwAAVKKkpCQlJiYabSWtGvzchQsX9Pjjj6tr165q3bq1t/3BBx9UkyZN1KBBA+3atUuTJ0/Wvn379D//8z+SJLfbbQQDSd7Xbre7TDUTDgAAqESlXUK4koSEBH3++ef6+OOPjfaxY8d6v27Tpo2ioqLUu3dvZWdnq1mzZtdcr8RlBQAAapzx48dr9erV+uijj9SwYcPLju3SpYskaf/+/ZIkp9OpnJwcY8zF16Xdp/BzhAMAAGoIy7I0fvx4rVy5Uhs2bFDTpk2vOCczM1OSFBUVJUlyuVzKyspSbm6ud0xKSoocDodiYmLKVAeXFQAAqCESEhK0YsUKvfvuuwoODvbeIxASEqLatWsrOztbK1as0IABA1SvXj3t2rVLEydOVPfu3dW2bVtJUlxcnGJiYjRy5EjNmDFDbrdbU6ZMUUJCQpkvb7ByAABADbFgwQLl5eWpZ8+eioqK8m5vvvmmJMnf31/r169XXFycoqOj9cQTT2jIkCFatWqVdx++vr5avXq1fH195XK5NGLECI0aNcp4LsKVsHIAAEANYVnWZfsbNWqktLS0K+6nSZMmWrNmzVXXwcoBAAAwEA4AAICBcAAAAAyEAwAAYCAcAAAAA+EAAAAYCAcAAMBAOAAAAAbCAQAAMBAOAACAgXAAAAAMhAMAAGAgHAAAAAPhAAAAGAgHAADAQDgAAAAGv+ouAACAmubD+f+uuJ3NHVRx+6oirBwAAAAD4QAAABgIBwAAwEA4AAAABsIBAAAwEA4AAICBcAAAAAyEAwAAYCAcAAAAA+EAAAAYCAcAAMBAOAAAAAbCAQAAMBAOAACAgXAAAAAMhAMAAGAgHAAAAAPhAAAAGAgHAADUEMnJyercubOCg4MVERGhwYMHa9++fcaYc+fOKSEhQfXq1VOdOnU0ZMgQ5eTkGGMOHTqk+Ph4BQYGKiIiQpMmTVJRUVGZ6yAcAABQQ6SlpSkhIUGffPKJUlJSdP78ecXFxamgoMA7ZuLEiVq1apXeeustpaWl6ciRI7r33nu9/cXFxYqPj1dhYaG2bt2qpUuXasmSJXr66afLXIfNsiyrQs/sKv3hD3+o7hKAGmeX/4DqLgGokdLnDqrU/ef5Tq2wfYUUX/2+jh07poiICKWlpal79+7Ky8tT/fr1tWLFCt13332SpC+++EKtWrVSenq6brvtNn3wwQe6++67deTIEUVGRkqSFi5cqMmTJ+vYsWPy9/e/4nFZOQAAoBJ5PB7l5+cbm8fjKdPcvLw8SVJYWJgkKSMjQ+fPn1efPn28Y6Kjo9W4cWOlp6dLktLT09WmTRtvMJCkvn37Kj8/X7t37y7TcQkHAABUouTkZIWEhBhbcnLyFedduHBBjz/+uLp27arWrVtLktxut/z9/RUaGmqMjYyMlNvt9o75aTC42H+xryz8yjQKAABclaSkJCUmJhptdrv9ivMSEhL0+eef6+OPP66s0kpFOAAAoBLZ7fYyhYGfGj9+vFavXq1NmzapYcOG3nan06nCwkKdOnXKWD3IycmR0+n0jtm+fbuxv4ufZrg45kq4rAAAQA1hWZbGjx+vlStXasOGDWratKnRHxsbq1q1aik1NdXbtm/fPh06dEgul0uS5HK5lJWVpdzcXO+YlJQUORwOxcTElKkOVg4AAKghEhIStGLFCr377rsKDg723iMQEhKi2rVrKyQkRGPGjFFiYqLCwsLkcDg0YcIEuVwu3XbbbZKkuLg4xcTEaOTIkZoxY4bcbremTJmihISEMq9gEA4AAKghFixYIEnq2bOn0f7KK6/ooYcekiTNnDlTPj4+GjJkiDwej/r27av58+d7x/r6+mr16tV65JFH5HK5FBQUpNGjR2vatGllroPnHAA1GM85AEp2vTznoLpwzwEAADAQDgAAgIFwAAAADIQDAABgIBwAAAAD4QAAABgIBwAAwEA4AAAABsIBAAAwEA4AAICBcAAAAAz84qXrwM0336y4uDg1btxYoaGhmj9/vj777DNjzMCBA3XHHXeodu3ays7O1ooVK7y/7rNevXoaMGCAoqOj5XA4lJeXp23btmnNmjUqLi6ujlMCrtmou25Wj3ZRahIZLM/5YmUdOKH57+7RodwzJY5/8ZHb5IqJ1OTF27Rpl9vbHlm3tib9tq1ibw7XWU+xPth2SAtW7VXxhRrxa2uAq8LKwXXA399f3377rV5//fUS+/v27as777xTy5cv1/PPPy+Px6NHH31Ufn4/Zken0ykfHx8tW7ZMzz77rP75z3+qe/fuGjx4cBWeBVCxOjSvp39tPqCH/98mPTZvq/x8bZqV4FKAv+8lY4f1ukkl/Yo6H5v0/8bdplq+Phr74mZNX/apBnRprIfjo6vgDIDKQzi4DuzevVvvvvuuMjMzS+zv3bu31qxZo88++0zfffedXnnlFYWGhqp9+/be+UuXLtXevXt1/Phx7dq1SykpKerQoUPVnQRQwSYu+ERrth3WAfdp7f8uX39e9m9FhQUqulGoMe7mGxx6oFdz/WX5vy/Zx62tInSjM1hTX/1UX32Xr0/25Grx+19oyB1N5edrq6IzASoe4eA6Fx4erpCQEO3du9fbdu7cOR04cEA33XRTqfNq166ts2fPVkWJQJWoE1BLkpR/ttDbZq/lq2dHd9ILb+3SidOeS+a0uTFM2UfydfInfZ98kas6tWvppihH5RcNVJIKDweHDx/W73//+8uO8Xg8ys/PNzauXVcPh+PH/8Dy8/ON9vz8fIWEhJQ4p379+urVq5c2bdpU6fUBVcFmkx4f0lqfZX+vr4+e9rY/fm9rZR04oc1Z7hLnhTnsl4SGE/kebx/wS1XhNySeOHFCS5cu1T/+8Y9SxyQnJ+vZZ5812jp27KhOnTpVdDmoYKGhoXr00UeVkZGhjz/+uLrLASrEk0Pb6qYoh/4wa7O3rVtrp2JbhGv0XzdWX2GoNv9+YGGF7aunplbYvqpKucPBe++9d9n+r7/++or7SEpKUmJiotH289eoGhdXDBwOh7F64HA4dPjwYWNsSEiIEhMTlZ2drWXLllVpnUBleWJoG3Vt7dQjsz/WsVPnvO2dWoTrhvAgrZsxwBj/3Jhb9Vn290qYs0Un8j2KaVLX6L+4YnBxBQH4JSp3OBg8eLBsNpuskm7d/V822+VvxLHb7bLbzSU3X99L7xBG5Tt+/Ljy8vIUHR2tb7/9VpIUEBCgpk2bKi0tzTsuNDRUiYmJ+uabb7R06dLL/vkDvxRPDG2jHm2j9J9ztujo9+Y9NK+mfKX30r8x2pb/152a/T+f6+PPf7zMkHXwhEb3baG6dfx18syP9yrc2rK+zvxwXgfcpwX8UpU7HERFRWn+/PkaNGhQif2ZmZmKjY295sJQcex2u+rXr+99HR4eroYNG6qgoEAnT55UamqqBgwYoNzcXB0/flyDBg3SqVOnvJ9uuBgMTpw4oX/9618KDg727uvn9yoAvxRP3t9WcbENNXnxNp09V6Sw4B/fsBScOy/P+Qs6cdpT4k2IOSfPeoPE9r25Oug+radHxWreu7tVL9iusXe30r82H9D5ogtVej5ARSp3OIiNjVVGRkap4eBKqwqoek2aNNETTzzhfX3//fdLkrZu3aqlS5dq7dq18vf314gRIxQYGKj9+/drzpw5KioqkiS1atVKkZGRioyM1F//+ldj33/4wx+q7kSACjTkjqaSpPmPdTPapy/7VGu2HS5pyiUuWNKTCz/RpN+20+LEO/SDp1gfbD+sxe9/UeH1AlXJZpXzJ/nmzZtVUFCgfv36ldhfUFCgnTt3qkePHuUqhB8ywKV2+Q+48iDgOpQ+t+Q3qBVl4whnhe2r57KSP+1Sk5V75eCOO+64bH9QUFC5gwEAAKg5eAgSAAAwEA4AAICBcAAAAAyEAwAAYCAcAAAAA+EAAAAYCAcAAMBAOAAAAAbCAQAAMBAOAACAgXAAAAAMhAMAAGAgHAAAAAPhAACAGmLTpk0aOHCgGjRoIJvNpnfeecfof+ihh2Sz2YytX79+xpgTJ05o+PDhcjgcCg0N1ZgxY3TmzJly1UE4AACghigoKFC7du00b968Usf069dPR48e9W6vv/660T98+HDt3r1bKSkpWr16tTZt2qSxY8eWqw6/q6oeAABUuP79+6t///6XHWO32+V0Okvs27t3rz788EPt2LFDnTp1kiTNnTtXAwYM0AsvvKAGDRqUqQ5WDgAAqEQej0f5+fnG5vF4rnp/GzduVEREhFq2bKlHHnlE33//vbcvPT1doaGh3mAgSX369JGPj4+2bdtW5mMQDgAAqETJyckKCQkxtuTk5KvaV79+/fTqq68qNTVVf/3rX5WWlqb+/furuLhYkuR2uxUREWHM8fPzU1hYmNxud5mPw2UFAAAqUVJSkhITE402u91+VfsaNmyY9+s2bdqobdu2atasmTZu3KjevXtfU50/xcoBAACVyG63y+FwGNvVhoOfu+mmmxQeHq79+/dLkpxOp3Jzc40xRUVFOnHiRKn3KZSEcAAAwC/Ut99+q++//15RUVGSJJfLpVOnTikjI8M7ZsOGDbpw4YK6dOlS5v1yWQEAgBrizJkz3lUASTpw4IAyMzMVFhamsLAwPfvssxoyZIicTqeys7P11FNPqXnz5urbt68kqVWrVurXr58efvhhLVy4UOfPn9f48eM1bNiwMn9SQWLlAACAGmPnzp3q0KGDOnToIElKTExUhw4d9PTTT8vX11e7du3SPffcoxYtWmjMmDGKjY3V5s2bjcsUy5cvV3R0tHr37q0BAwaoW7du+vvf/16uOlg5AACghujZs6csyyq1f+3atVfcR1hYmFasWHFNdbByAAAADIQDAABgIBwAAAAD4QAAABgIBwAAwEA4AAAABsIBAAAwEA4AAICBcAAAAAyEAwAAYCAcAAAAA+EAAAAYCAcAAMBAOAAAAAbCAQAAMPhVdwEAANQ0rwcNqrB99aywPVUdVg4AAICBcAAAAAyEAwAAYCAcAAAAA+EAAAAYCAcAAMBAOAAAAAbCAQAAMBAOAACAgXAAAAAMhAMAAGAgHAAAAAPhAAAAGAgHAADAQDgAAAAGwgEAADAQDgAAgIFwAAAADIQDAABgIBwAAAAD4QAAgBpi06ZNGjhwoBo0aCCbzaZ33nnH6LcsS08//bSioqJUu3Zt9enTR1999ZUx5sSJExo+fLgcDodCQ0M1ZswYnTlzplx1EA4AAKghCgoK1K5dO82bN6/E/hkzZmjOnDlauHChtm3bpqCgIPXt21fnzp3zjhk+fLh2796tlJQUrV69Wps2bdLYsWPLVYffNZ0FAACoMP3791f//v1L7LMsS7NmzdKUKVM0aNAgSdKrr76qyMhIvfPOOxo2bJj27t2rDz/8UDt27FCnTp0kSXPnztWAAQP0wgsvqEGDBmWqg5UDAAAqkcfjUX5+vrF5PJ5y7+fAgQNyu93q06ePty0kJERdunRRenq6JCk9PV2hoaHeYCBJffr0kY+Pj7Zt21bmYxEOAACoRMnJyQoJCTG25OTkcu/H7XZLkiIjI432yMhIb5/b7VZERITR7+fnp7CwMO+YsuCyAgAAlSgpKUmJiYlGm91ur6ZqyoZwAABAJbLb7RUSBpxOpyQpJydHUVFR3vacnBy1b9/eOyY3N9eYV1RUpBMnTnjnlwWXFQAA+AVo2rSpnE6nUlNTvW35+fnatm2bXC6XJMnlcunUqVPKyMjwjtmwYYMuXLigLl26lPlYrBwAAFBDnDlzRvv37/e+PnDggDIzMxUWFqbGjRvr8ccf15///GfdfPPNatq0qf70pz+pQYMGGjx4sCSpVatW6tevnx5++GEtXLhQ58+f1/jx4zVs2LAyf1JBIhwAAFBj7Ny5U7169fK+vnivwujRo7VkyRI99dRTKigo0NixY3Xq1Cl169ZNH374oQICArxzli9frvHjx6t3797y8fHRkCFDNGfOnHLVQTgAAKCG6NmzpyzLKrXfZrNp2rRpmjZtWqljwsLCtGLFimuqg3sOAACAgXAAAAAMhAMAAGAgHAAAAAPhAAAAGAgHAADAQDgAAAAGwgEAADAQDgAAgIFwAAAADIQDAABgIBwAAAAD4QAAABgIBwAAwGCzLve7IXHd8Xg8Sk5OVlJSkux2e3WXA9QI/LvA9YZwAEN+fr5CQkKUl5cnh8NR3eUANQL/LnC94bICAAAwEA4AAICBcAAAAAyEAxjsdrueeeYZbroCfoJ/F7jecEMiAAAwsHIAAAAMhAMAAGAgHAAAAAPhAAAAGAgHAADAQDiA17x583TjjTcqICBAXbp00fbt26u7JKBabdq0SQMHDlSDBg1ks9n0zjvvVHdJQJUgHECS9OabbyoxMVHPPPOMPv30U7Vr1059+/ZVbm5udZcGVJuCggK1a9dO8+bNq+5SgCrFcw4gSerSpYs6d+6sl156SZJ04cIFNWrUSBMmTNAf//jHaq4OqH42m00rV67U4MGDq7sUoNKxcgAVFhYqIyNDffr08bb5+PioT58+Sk9Pr8bKAADVgXAAHT9+XMXFxYqMjDTaIyMj5Xa7q6kqAEB1IRwAAAAD4QAKDw+Xr6+vcnJyjPacnBw5nc5qqgoAUF0IB5C/v79iY2OVmprqbbtw4YJSU1PlcrmqsTIAQHXwq+4CUDMkJiZq9OjR6tSpk2699VbNmjVLBQUF+t3vflfdpQHV5syZM9q/f7/39YEDB5SZmamwsDA1bty4GisDKhcfZYTXSy+9pL/97W9yu91q37695syZoy5dulR3WUC12bhxo3r16nVJ++jRo7VkyZKqLwioIoQDAABg4J4DAABgIBwAAAAD4QAAABgIBwAAwEA4AAAABsIBAAAwEA4AAICBcAAAAAyEAwAAYCAcAAAAA+EAAAAY/j/mu1HBs0hZRAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#################################### KNN ########################\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "model =  KNeighborsClassifier()\n",
    "model.fit(X_train,y_train)\n",
    "prediction_knn=model.predict(X_test)\n",
    "\n",
    "print('--------KNN -------')\n",
    "print('The accuracy KNN Classifier is',round(accuracy_score(prediction_knn,y_test)*100,2))\n",
    "\n",
    "kfold = KFold(n_splits=10,shuffle=True, random_state=42) # split the data into 8 equal parts\n",
    "\n",
    "result_knn=cross_val_score(model,X,y,cv=10,scoring='accuracy')\n",
    "\n",
    "print('The cross validated score for KNN classifier is:',round(result_knn.mean()*100,2))\n",
    "\n",
    "y_pred = cross_val_predict(model,X,y,cv=10)\n",
    "sns.heatmap(confusion_matrix(y,y_pred),annot=True,fmt='3.0f',cmap=\"Accent_r\")\n",
    "plt.title('Confusion Matrix', y=1, size=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b151affa",
   "metadata": {},
   "source": [
    "### 逻辑回归Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "ee6be172",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T08:40:14.836122Z",
     "start_time": "2022-04-27T08:40:12.591162Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------Logistic Regression -------\n",
      "The accuracy Logistic Regression is 81.72\n",
      "The cross validated score for Logistic Regression is: 78.56\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#################################### Logistic Regression ########################\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model =  LogisticRegression()\n",
    "model.fit(X_train,y_train)\n",
    "prediction_lr=model.predict(X_test)\n",
    "\n",
    "print('--------Logistic Regression -------')\n",
    "print('The accuracy Logistic Regression is',round(accuracy_score(prediction_lr,y_test)*100,2))\n",
    "\n",
    "kfold = KFold(n_splits=10,shuffle=True, random_state=42) # split the data into 10 equal parts\n",
    "\n",
    "result_lr=cross_val_score(model,X,y,cv=10,scoring='accuracy')\n",
    "\n",
    "print('The cross validated score for Logistic Regression is:',round(result_lr.mean()*100,2))\n",
    "\n",
    "y_pred = cross_val_predict(model,X,y,cv=10)\n",
    "sns.heatmap(confusion_matrix(y,y_pred),annot=True,fmt='3.0f',cmap=\"Accent_r\")\n",
    "plt.title('Confusion Matrix', y=1, size=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e207e4f",
   "metadata": {},
   "source": [
    "### 随机森林 Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4e3c336",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T08:43:46.283748Z",
     "start_time": "2022-04-27T08:42:24.831619Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------Random Forest Classifier -------\n",
      "The accuracy Random Forest Classifier  is 78.36\n",
      "The cross validated score for Random Forest Classifier is: 80.93\n"
     ]
    }
   ],
   "source": [
    "# Random Forests\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model = RandomForestClassifier(n_estimators=800,\n",
    "                             min_samples_split=12,\n",
    "                             # max_features='auto',\n",
    "                            max_features='sqrt',\n",
    "                             oob_score=True,\n",
    "                             random_state=1,n_jobs=-1)\n",
    "\n",
    "model.fit(X_train,y_train)\n",
    "prediction_rf=model.predict(X_test)\n",
    "\n",
    "print('--------Random Forest Classifier -------')\n",
    "print('The accuracy Random Forest Classifier  is',round(accuracy_score(prediction_rf,y_test)*100,2))\n",
    "\n",
    "kfold = KFold(n_splits=10,shuffle=True, random_state=42) # split the data into 10 equal parts\n",
    "\n",
    "result_rf=cross_val_score(model,X,y,cv=10,scoring='accuracy')\n",
    "\n",
    "print('The cross validated score for Random Forest Classifier is:',round(result_rf.mean()*100,2))\n",
    "\n",
    "y_pred = cross_val_predict(model,X,y,cv=10)\n",
    "sns.heatmap(confusion_matrix(y,y_pred),annot=True,fmt='3.0f',cmap=\"Accent_r\")\n",
    "plt.title('Confusion Matrix', y=1, size=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fef2c38",
   "metadata": {},
   "source": [
    "### Gradient Boost"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1d4b6b4",
   "metadata": {},
   "source": [
    "### ADA Boost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca6de31d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T08:45:13.955909Z",
     "start_time": "2022-04-27T08:45:08.089739Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "model= AdaBoostClassifier()\n",
    "\n",
    "model.fit(X_train,y_train)\n",
    "prediction_ada=model.predict(X_test)\n",
    "\n",
    "print('--------ADA Boost -------')\n",
    "print('The accuracy ADA Boost Classifier is',round(accuracy_score(prediction_ada,y_test)*100,2))\n",
    "\n",
    "kfold = KFold(n_splits=10,shuffle=True, random_state=42) # split the data into 10 equal parts\n",
    "\n",
    "result_ada=cross_val_score(model,X,y,cv=10,scoring='accuracy')\n",
    "\n",
    "print('The cross validated score for ADA Boost classifier is:',round(result_ada.mean()*100,2))\n",
    "\n",
    "y_pred = cross_val_predict(model,X,y,cv=10)\n",
    "sns.heatmap(confusion_matrix(y,y_pred),annot=True,fmt='3.0f',cmap=\"Accent_r\")\n",
    "plt.title('Confusion Matrix', y=1, size=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7a668f4",
   "metadata": {},
   "source": [
    "### 以上几种算法模型的评估对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32744c08",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T08:46:16.628995Z",
     "start_time": "2022-04-27T08:46:16.599733Z"
    }
   },
   "outputs": [],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', \n",
    "              'Random Forest', 'Naive Bayes', 'AdaBoostClassifier', \n",
    "              'Gradient Boosting'],\n",
    "    'Score': [result_svm.mean(), result_knn.mean(), result_lr.mean(), \n",
    "              result_rf.mean(), result_gnb.mean(), result_ada.mean(), \n",
    "              result_gb.mean()]})\n",
    "models.sort_values(by='Score',ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d27bff78",
   "metadata": {},
   "source": [
    "## 选择准确率最高的Random Forest算法，进行超参数调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27cab8c7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T08:50:15.403604Z",
     "start_time": "2022-04-27T08:47:51.630513Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "n_estim=range(100,1000,100)\n",
    "max_depth = range(5,15,2)\n",
    "\n",
    "param_grid = {\"n_estimators\" :n_estim,\"max_depth\":max_depth}\n",
    "model = RandomForestClassifier()\n",
    "model_rf = GridSearchCV(model,param_grid = param_grid, cv=10, scoring=\"accuracy\", n_jobs= 4, verbose = 1)\n",
    "\n",
    "model_rf.fit(X_train,y_train)\n",
    "\n",
    "print(model_rf.best_score_)\n",
    "\n",
    "#best estimator\n",
    "print(model_rf.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8723dc7b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T09:15:15.405091Z",
     "start_time": "2022-04-27T09:15:14.361829Z"
    }
   },
   "outputs": [],
   "source": [
    "### Applying Param Got From GridSearchCV\n",
    "rfmodel = RandomForestClassifier(max_depth=5, n_estimators=700, min_samples_split=12, n_jobs=-1,\n",
    "                       oob_score=True, random_state=11)\n",
    "\n",
    "rfmodel.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "447b8e07",
   "metadata": {},
   "source": [
    "## 使用Random Forest算法调优后的模型进行预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fffed77b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T09:14:42.934909Z",
     "start_time": "2022-04-27T09:14:42.864280Z"
    }
   },
   "outputs": [],
   "source": [
    "test = clean_data(df_test)\n",
    "# test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43d6351d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T09:15:22.433156Z",
     "start_time": "2022-04-27T09:15:22.270394Z"
    }
   },
   "outputs": [],
   "source": [
    "y_pred = rfmodel.predict(test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0192e5c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T09:15:25.580631Z",
     "start_time": "2022-04-27T09:15:25.569039Z"
    }
   },
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": df_test[\"PassengerId\"],\n",
    "        \"Survived\": y_pred})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58156744",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-27T09:15:29.320922Z",
     "start_time": "2022-04-27T09:15:29.309385Z"
    }
   },
   "outputs": [],
   "source": [
    "submission.to_csv('Submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94e0f9fe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9f3a4f72-63d9-43c8-8bca-a27ca7eb158c",
   "metadata": {},
   "source": [
    "## Model Training - 进阶版"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "576cd909-54e5-4060-afbc-b7273d9167e5",
   "metadata": {},
   "source": [
    "### 选择逻辑回归算法，进行超参数调优"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c82314e-c306-44b2-ae1b-96dbd4cfcbb2",
   "metadata": {},
   "source": [
    "#### 网格搜索法GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46b3c2d3-442e-4a7b-acef-89c61932f00e",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "class sklearn.linear_model.LogisticRegression(penalty='l2', *, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver='lbfgs', max_iter=100, multi_class='auto', verbose=0, warm_start=False, n_jobs=None, l1_ratio=None)\n",
    "\n",
    "这是Scikit-learn中LogisticRegression类的构造函数参数列表及其默认值：\n",
    "\n",
    "penalty：正则化类型，默认为'l2'，可选值为{'l1', 'l2', 'elasticnet', 'none'}。  \n",
    "dual：对偶形式，默认为False。对于n_samples > n_features的情况，通常选择False。  \n",
    "tol：收敛判据，默认为0.0001。如果迭代过程中损失函数的变化小于该值，则认为优化已经收敛。    \n",
    "C：正则化强度的倒数，默认为1.0。较小的值表示更强的正则化。  \n",
    "fit_intercept：是否拟合截距，默认为True。如果为True，则会为模型添加一个截距项。  \n",
    "intercept_scaling：截距项的缩放因子，默认为1。  \n",
    "class_weight：类别权重，默认为None。可以用于处理类别不平衡的问题。  \n",
    "random_state：随机数种子，默认为None。  \n",
    "solver：优化算法，默认为'lbfgs'。其他可选值有{'newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'}。  \n",
    "max_iter：最大迭代次数，默认为100。当优化算法没有收敛时，控制最大迭代次数。\n",
    "multi_class：多类别分类策略，默认为'auto'。可选值为{'auto', 'ovr', 'multinomial'}。\n",
    "verbose：详细程度，默认为0。如果大于0，会输出优化过程的信息。\n",
    "warm_start：是否热启动，默认为False。如果设置为True，则重用上一次调用的解决方案以适合初始化；否则，将擦除先前的解决方案。\n",
    "n_jobs：并行工作数，默认为None。如果设置为-1，则使用所有可用的CPU。\n",
    "l1_ratio：Elastic-Net混合参数，默认为None。当penalty='elasticnet'时使用，指定L1和L2正则化的混合比例。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "961d5e8a-3143-4663-b592-7632d205d95f",
   "metadata": {},
   "source": [
    "class sklearn.model_selection.GridSearchCV(estimator, param_grid, *, scoring=None, n_jobs=None, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', error_score=nan, return_train_score=False)\n",
    "\n",
    "GridSearchCV是Scikit-learn中用于超参数搜索的一种方法，它允许你指定一个参数网格，并在指定的参数空间内进行穷举搜索。\n",
    "\n",
    "estimator：你想要优化的模型对象。通常是一个Scikit-learn中的机器学习算法，比如LogisticRegression、RandomForestClassifier等。  \n",
    "param_grid：一个字典或字典列表，指定了要搜索的参数网格。字典的键是模型参数的名称，值是要搜索的参数值的列表。  \n",
    "scoring：模型评估指标。用于评估模型性能的指标，默认为None，即使用模型的score方法来评估性能。  \n",
    "n_jobs：并行工作数。指定在搜索过程中并行运行的作业数量。默认为None，表示不并行运行。  \n",
    "refit：是否在找到最佳参数后使用整个数据集重新拟合模型。默认为True。  \n",
    "cv：交叉验证生成器或迭代器。用于确定交叉验证的折数和分割策略。  \n",
    "verbose：详细程度。控制输出信息的详细程度。默认为0，表示不输出额外信息。  \n",
    "pre_dispatch：预调度。用于控制在并行运行时发送的作业数量。默认为'2*n_jobs'，表示发送两倍于可用CPU核心数量的作业。  \n",
    "error_score：错误得分。当一个参数设置导致错误时，用于替代该参数设置的得分。默认为NaN，表示忽略错误。  \n",
    "return_train_score：是否返回训练集的得分。默认为False，表示不返回训练集的得分。  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "7d5dd230-137b-4160-8a25-23dbf0d7343d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_data(data):\n",
    "    # Let's Start By Dropping `PassengerID` `Name` `Ticket` `Cabin` \n",
    "    # 删除关联性不大的`PassengerID` `Name` `Ticket` `Cabin`列\n",
    "    data = data.drop(['PassengerId','Name','Ticket','Cabin'],axis=1)\n",
    "    \n",
    "    # Converting Age Into Four Different Classes 'Children', 'Teenage','Adult', 'old'\n",
    "    # 根据年龄分为四个不同的类别“儿童”、“青少年”、“成人”、“老年”\n",
    "    # https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.cut.html    \n",
    "    data['Age'] = pd.cut(data['Age'], bins=[0,12,20,40,120], labels=['Children','Teenage','Adult','Elder'])\n",
    "    \n",
    "    # Converting Fare Into Four Distinct Categories 'Low_fare','median_fare','Average_fare','high_fare'\n",
    "    # 根据票价转换为四个不同的类别 'Low_fare'、'median_fare'、'Average_fare'、'high_fare'\n",
    "    data['Fare'] = pd.cut(data['Fare'], bins=[0,7.91,14.45,31,120], labels=['Low_fare','median_fare','Average_fare','high_fare'])\n",
    "    \n",
    "    #  Getting OneHotEncoding For Categorical Columns ['Age','Fare','Sex','Embarked']\n",
    "    # get_dummies函数进行one-hot编码， 比如Sex分为female和male，那么分为Sex_female和Sex_male，（1，0）表示Female\n",
    "    # https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.get_dummies.html\n",
    "    data = pd.get_dummies(data, columns = [\"Sex\",\"Age\",\"Embarked\",\"Fare\"], dtype=int)\n",
    "    \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "3308aef8-96ce-4578-a276-c2c012110a45",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass  SibSp  Parch  Sex_female  Sex_male  Age_Children  \\\n",
       "0           0       3      1      0           0         1             0   \n",
       "1           1       1      1      0           1         0             0   \n",
       "2           1       3      0      0           1         0             0   \n",
       "3           1       1      1      0           1         0             0   \n",
       "4           0       3      0      0           0         1             0   \n",
       "..        ...     ...    ...    ...         ...       ...           ...   \n",
       "886         0       2      0      0           0         1             0   \n",
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       "888         0       3      1      2           1         0             0   \n",
       "889         1       1      0      0           0         1             0   \n",
       "890         0       3      0      0           0         1             0   \n",
       "\n",
       "     Age_Teenage  Age_Adult  Age_Elder  Embarked_C  Embarked_Q  Embarked_S  \\\n",
       "0              0          1          0           0           0           1   \n",
       "1              0          1          0           1           0           0   \n",
       "2              0          1          0           0           0           1   \n",
       "3              0          1          0           0           0           1   \n",
       "4              0          1          0           0           0           1   \n",
       "..           ...        ...        ...         ...         ...         ...   \n",
       "886            0          1          0           0           0           1   \n",
       "887            1          0          0           0           0           1   \n",
       "888            0          0          0           0           0           1   \n",
       "889            0          1          0           1           0           0   \n",
       "890            0          1          0           0           1           0   \n",
       "\n",
       "     Fare_Low_fare  Fare_median_fare  Fare_Average_fare  Fare_high_fare  \n",
       "0                1                 0                  0               0  \n",
       "1                0                 0                  0               1  \n",
       "2                0                 1                  0               0  \n",
       "3                0                 0                  0               1  \n",
       "4                0                 1                  0               0  \n",
       "..             ...               ...                ...             ...  \n",
       "886              0                 1                  0               0  \n",
       "887              0                 0                  1               0  \n",
       "888              0                 0                  1               0  \n",
       "889              0                 0                  1               0  \n",
       "890              1                 0                  0               0  \n",
       "\n",
       "[891 rows x 17 columns]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "\n",
    "df_labeled = pd.read_csv(r'DataSet\\Titanic\\LabeledData.csv')\n",
    "df_labeled = clean_data(df_labeled)\n",
    "df_labeled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "dfdb0fa2-d873-4543-9b5d-4edb0250b9dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_labeled.drop('Survived',axis=1)\n",
    "y = df_labeled['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "341edbc4-6da3-4596-ab47-97adfd5662e6",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 16 columns</p>\n",
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      ],
      "text/plain": [
       "     Pclass  SibSp  Parch  Sex_female  Sex_male  Age_Children  Age_Teenage  \\\n",
       "0         3      1      0           0         1             0            0   \n",
       "1         1      1      0           1         0             0            0   \n",
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       "889       1      0      0           0         1             0            0   \n",
       "890       3      0      0           0         1             0            0   \n",
       "\n",
       "     Age_Adult  Age_Elder  Embarked_C  Embarked_Q  Embarked_S  Fare_Low_fare  \\\n",
       "0            1          0           0           0           1              1   \n",
       "1            1          0           1           0           0              0   \n",
       "2            1          0           0           0           1              0   \n",
       "3            1          0           0           0           1              0   \n",
       "4            1          0           0           0           1              0   \n",
       "..         ...        ...         ...         ...         ...            ...   \n",
       "886          1          0           0           0           1              0   \n",
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       "888          0          0           0           0           1              0   \n",
       "889          1          0           1           0           0              0   \n",
       "890          1          0           0           1           0              1   \n",
       "\n",
       "     Fare_median_fare  Fare_Average_fare  Fare_high_fare  \n",
       "0                   0                  0               0  \n",
       "1                   0                  0               1  \n",
       "2                   1                  0               0  \n",
       "3                   0                  0               1  \n",
       "4                   1                  0               0  \n",
       "..                ...                ...             ...  \n",
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       "888                 0                  1               0  \n",
       "889                 0                  1               0  \n",
       "890                 0                  0               0  \n",
       "\n",
       "[891 rows x 16 columns]"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "05e0fc8b-d418-4d26-87f2-840feada48bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      0\n",
       "      ..\n",
       "886    0\n",
       "887    1\n",
       "888    0\n",
       "889    1\n",
       "890    0\n",
       "Name: Survived, Length: 891, dtype: int64"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "46fa3af8-df06-484c-8640-a090b5f09dff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split \n",
    "X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.15, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "fa90f6eb-3b71-46a0-9671-a2df1e398bf3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>757 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  SibSp  Parch  Sex_female  Sex_male  Age_Children  Age_Teenage  \\\n",
       "599       1      1      0           0         1             0            0   \n",
       "830       3      1      0           1         0             0            1   \n",
       "306       1      0      0           1         0             0            0   \n",
       "231       3      0      0           0         1             0            0   \n",
       "845       3      0      0           0         1             0            0   \n",
       "..      ...    ...    ...         ...       ...           ...          ...   \n",
       "106       3      0      0           1         0             0            0   \n",
       "270       1      0      0           0         1             0            0   \n",
       "860       3      2      0           0         1             0            0   \n",
       "435       1      1      2           1         0             0            1   \n",
       "102       1      0      1           0         1             0            0   \n",
       "\n",
       "     Age_Adult  Age_Elder  Embarked_C  Embarked_Q  Embarked_S  Fare_Low_fare  \\\n",
       "599          0          1           1           0           0              0   \n",
       "830          0          0           1           0           0              0   \n",
       "306          0          0           1           0           0              0   \n",
       "231          1          0           0           0           1              1   \n",
       "845          0          1           0           0           1              1   \n",
       "..         ...        ...         ...         ...         ...            ...   \n",
       "106          1          0           0           0           1              1   \n",
       "270          0          0           0           0           1              0   \n",
       "860          0          1           0           0           1              0   \n",
       "435          0          0           0           0           1              0   \n",
       "102          1          0           0           0           1              0   \n",
       "\n",
       "     Fare_median_fare  Fare_Average_fare  Fare_high_fare  \n",
       "599                 0                  0               1  \n",
       "830                 0                  1               0  \n",
       "306                 0                  0               1  \n",
       "231                 0                  0               0  \n",
       "845                 0                  0               0  \n",
       "..                ...                ...             ...  \n",
       "106                 0                  0               0  \n",
       "270                 0                  1               0  \n",
       "860                 1                  0               0  \n",
       "435                 0                  0               1  \n",
       "102                 0                  0               1  \n",
       "\n",
       "[757 rows x 16 columns]"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_trainval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "4b0a2ad4-defd-4b82-96e8-4fab8d65d77f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X_trainval, y_trainval, test_size=0.15, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "7a1e0760-e4dc-45ec-a2bb-92ecb4ada103",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "</table>\n",
       "<p>643 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  SibSp  Parch  Sex_female  Sex_male  Age_Children  Age_Teenage  \\\n",
       "868       3      0      0           0         1             0            0   \n",
       "223       3      0      0           0         1             0            0   \n",
       "846       3      8      2           0         1             0            0   \n",
       "171       3      4      1           0         1             1            0   \n",
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       "..      ...    ...    ...         ...       ...           ...          ...   \n",
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       "283       3      0      0           0         1             0            1   \n",
       "829       1      0      0           1         0             0            0   \n",
       "\n",
       "     Age_Adult  Age_Elder  Embarked_C  Embarked_Q  Embarked_S  Fare_Low_fare  \\\n",
       "868          0          0           0           0           1              0   \n",
       "223          0          0           0           0           1              1   \n",
       "846          0          0           0           0           1              0   \n",
       "171          0          0           0           1           0              0   \n",
       "435          0          0           0           0           1              0   \n",
       "..         ...        ...         ...         ...         ...            ...   \n",
       "533          0          0           1           0           0              0   \n",
       "302          0          0           0           0           1              0   \n",
       "473          1          0           1           0           0              0   \n",
       "283          0          0           0           0           1              0   \n",
       "829          0          1           0           0           0              0   \n",
       "\n",
       "     Fare_median_fare  Fare_Average_fare  Fare_high_fare  \n",
       "868                 1                  0               0  \n",
       "223                 0                  0               0  \n",
       "846                 0                  0               1  \n",
       "171                 0                  1               0  \n",
       "435                 0                  0               1  \n",
       "..                ...                ...             ...  \n",
       "533                 0                  1               0  \n",
       "302                 0                  0               0  \n",
       "473                 1                  0               0  \n",
       "283                 1                  0               0  \n",
       "829                 0                  0               1  \n",
       "\n",
       "[643 rows x 16 columns]"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "d8cff7dd-2aa1-45fb-80f8-060042755662",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Set准确率: 0.7982456140350878\n",
      "Best parameters found:  {'C': 100, 'penalty': 'l2'}\n",
      "Best cross-test score:  0.8054086538461538\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\ProgramData\\Python312\\env\\sklearn\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:547: FitFailedWarning: \n",
      "60 fits failed out of a total of 120.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "60 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"E:\\ProgramData\\Python312\\env\\sklearn\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"E:\\ProgramData\\Python312\\env\\sklearn\\Lib\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"E:\\ProgramData\\Python312\\env\\sklearn\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1172, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"E:\\ProgramData\\Python312\\env\\sklearn\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 67, in _check_solver\n",
      "    raise ValueError(\n",
      "ValueError: Solver lbfgs supports only 'l2' or None penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "E:\\ProgramData\\Python312\\env\\sklearn\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1051: UserWarning: One or more of the test scores are non-finite: [       nan 0.62519231        nan 0.78846154        nan 0.79764423\n",
      "        nan 0.79451923        nan 0.80384615        nan 0.80540865]\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix, classification_report\n",
    "from sklearn.model_selection import KFold, cross_val_score, cross_val_predict ## Cross Validation\n",
    "\n",
    "# 定义逻辑回归模型\n",
    "model = LogisticRegression()\n",
    "\n",
    "# 定义超参数网格\n",
    "param_grid = {\n",
    "    'penalty': ['l1', 'l2'],\n",
    "    'C': [0.001, 0.01, 0.1, 1, 10, 100]\n",
    "}\n",
    "\n",
    "# 使用网格搜索法进行超参数寻优\n",
    "grid_search = GridSearchCV(model, param_grid, cv=10)\n",
    "\n",
    "# 在Train Set上训练模型\n",
    "grid_search.fit(X_train,y_train)\n",
    "\n",
    "\n",
    "# 在Validation Set上评估模型性能\n",
    "best_model = grid_search.best_estimator_\n",
    "val_predictions = best_model.predict(X_val)\n",
    "val_accuracy = accuracy_score(y_val, val_predictions)\n",
    "\n",
    "\n",
    "# 5. 选择最佳超参数组合\n",
    "# 输出最佳超参数组合和对应的得分\n",
    "print(\"Validation Set准确率:\", val_accuracy)\n",
    "print(\"Best parameters found: \", grid_search.best_params_)\n",
    "print(\"Best cross-test score: \", grid_search.best_score_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "6f29c056-6490-4b97-90a3-b8746f7d7ab2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8059701492537313\n"
     ]
    }
   ],
   "source": [
    "### Applying Param Got From GridSearchCV\n",
    "best_model = LogisticRegression(penalty='l2', C=100)\n",
    "\n",
    "# best_model.fit(X_train,y_train)\n",
    "\n",
    "# 应用最优参数组合到Test Set\n",
    "best_model.fit(X_trainval, y_trainval)\n",
    "test_predictions = best_model.predict(X_test)\n",
    "test_accuracy = accuracy_score(y_test, test_predictions)\n",
    "\n",
    "print(test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "84076102-6412-460e-b028-46c35732d12b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross validated score for  classifier is: 79.8\n"
     ]
    }
   ],
   "source": [
    "result =cross_val_score(best_model,X,y,cv=10,scoring='accuracy')  # cv=10 表示10折\n",
    "print('The cross validated score for  classifier is:',round(result.mean()*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "9f0854ed-ddbc-4765-bc8f-fd64b1f9b53d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取需要预测的数据 UnLabeledData.csv\n",
    "df_unLabeled = pd.read_csv(r'DataSet\\Titanic\\UnlabeledData.csv')\n",
    "test = clean_data(df_unLabeled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "bf98c47d-2044-4194-bfc1-1fb1eb3f7012",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
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       "       0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1,\n",
       "       0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,\n",
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       "       0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0,\n",
       "       1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0,\n",
       "       0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
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       "       0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = best_model.predict(test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "7f95c84c-d3bf-47e3-8cc4-435009f4f240",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": df_unLabeled[\"PassengerId\"],\n",
    "        \"Survived\": y_pred})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "d33134a1-1657-42ab-859c-0dfbf29ea687",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('Submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eea9917-01c5-417a-91c0-1fbbe30c0cce",
   "metadata": {},
   "source": [
    "#### 随机网格搜索RamdomizedSeaarchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74729fa8-7bb3-4641-b236-cd614ec13482",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import uniform\n",
    "\n",
    "# 定义逻辑回归模型\n",
    "model = LogisticRegression()\n",
    "\n",
    "# 定义超参数网格\n",
    "param_dist = {\n",
    "    'penalty': ['l1', 'l2'],\n",
    "    'C': uniform(loc=0, scale=4)  # 在区间[0, 4)内均匀分布\n",
    "}\n",
    "\n",
    "# 使用网格搜索法进行超参数寻优\n",
    "grid_search = GridSearchCV(model, param_grid, cv=10)\n",
    "grid_search.fit(X_train,y_train)\n",
    "\n",
    "random_search = RandomizedSearchCV(model, param_distributions=param_dist, n_iter=10, cv=5)\n",
    "random_search.fit(X, y)\n",
    "\n",
    "\n",
    "# 输出最佳超参数组合和对应的得分\n",
    "print(\"Best parameters found: \", grid_search.best_params_)\n",
    "print(\"Best cross-test score: \", grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b98bcce6-0d44-4fcf-b7dc-1f79bee7981e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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